Bump brace-expansion from 1.1.18 to 1.1.21 in /docs (#24596)

Bumps [brace-expansion](https://github.com/juliangruber/brace-expansion) from 1.1.18 to 1.1.21.
- [Release notes](https://github.com/juliangruber/brace-expansion/releases)
- [Commits](https://github.com/juliangruber/brace-expansion/compare/v1.1.18...v1.1.21)

---
updated-dependencies:
- dependency-name: brace-expansion
  dependency-version: 1.1.21
  dependency-type: indirect
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

.github/actions/setup/action.yml

@@ -17,9 +17,14 @@ runs:
       shell: bash
     # This creates a virtual volume at /var/lib/docker to maximize the size
     # As of 2/14/2024, this results in 97G for docker images
+    # Runners no longer have a separate /mnt disk, so the temp PV is also carved
+    # from root and temp-reserve-mb is what actually stays free on root. Keep 4G
+    # there for setup-qemu/buildx caches in ~/.docker and the tool cache
     - name: Maximize build space
       uses: easimon/maximize-build-space@master
       with:
+        root-reserve-mb: 8192
+        temp-reserve-mb: 4096
         remove-dotnet: 'true'
         remove-android: 'true'
         remove-haskell: 'true'

docker/main/requirements-wheels.txt

@@ -2,9 +2,9 @@ aiofiles == 25.1.*
 click == 8.5.*
 # FastAPI
 aiohttp == 3.12.*
-starlette == 0.47.*
+starlette == 1.7.*
 starlette-context == 0.5.*
-fastapi[standard-no-fastapi-cloud-cli] == 0.116.*
+fastapi[standard-no-fastapi-cloud-cli] == 0.142.*
 uvicorn == 0.52.*
 slowapi == 0.1.*
 joserfc == 1.6.*

docker/main/rootfs/usr/local/go2rtc/create_config.py

@@ -15,6 +15,7 @@ from frigate.const import (
 )
 from frigate.ffmpeg_presets import parse_preset_hardware_acceleration_encode
 from frigate.util.config import find_config_file, resolve_ffmpeg_path
+from frigate.util.live_streams import raw_transcode_streams
 from frigate.util.services import (
     is_go2rtc_arbitrary_exec_allowed,
     is_restricted_go2rtc_source,
@@ -174,6 +175,17 @@ for name in list(go2rtc_config.get("streams", {})):
             del go2rtc_config["streams"][name]
             continue
 
+# add transcoded live streams; a user stream with the same name wins here and
+# fails Frigate's config validation
+transcoded_streams = raw_transcode_streams(config)
+
+if transcoded_streams:
+    if go2rtc_config.get("streams") is None:
+        go2rtc_config["streams"] = {}
+
+    for name, source in transcoded_streams.items():
+        go2rtc_config["streams"].setdefault(name, source)
+
 # add birdseye restream stream if enabled
 if config.get("birdseye", {}).get("restream", False):
     birdseye: dict[str, Any] = config.get("birdseye")

docs/data/object_detectors_models.yaml

@@ -824,6 +824,38 @@ cpu:
         models:
           - devices:
               - cpu:3
+xdna2:
+  title: AMD XDNA2
+  models:
+    - key: yolov9
+      label: YOLOv9
+      recommended: true
+      download: |-
+        Prepare the model using the frigate-xdna setup instructions linked above. For local YOLO models, Frigate must have access to the same ONNX file bytes as the sidecar. The example below uses YOLOv9-C at 320x320. Frigate+ models may instead use the same `plus://MODEL_ID` in Frigate and the sidecar.
+      ui: |-
+        Navigate to **Settings > System > Detection models** and add a model. The ZMQ endpoint is not reported by the hardware probe, so set `devices` to `zmq:tcp://xdna:5555` in YAML. Then, on the same model, open the **Custom Model** tab and configure:
+
+        | Field                                    | Value                                      |
+        | ---------------------------------------- | ------------------------------------------ |
+        | **Custom object detector model path**    | `/config/models/yolov9-c-320.onnx`       |
+        | **Label map for custom object detector** | `/labelmap/coco-80.txt`                   |
+        | **Object detection model input width**   | `320`                                    |
+        | **Object detection model input height**  | `320`                                    |
+        | **Model Input Pixel Color Format**       | `rgb` (Frigate's default value)          |
+        | **Model Input Tensor Shape**             | `nchw`                                   |
+        | **Model Input D Type**                   | `float`                                  |
+        | **Object Detection Model Type**          | `yolo-generic`                           |
+      yaml: |-
+        models:
+          - devices:
+              - zmq:tcp://xdna:5555
+            model_type: yolo-generic
+            width: 320
+            height: 320
+            input_tensor: nchw
+            input_dtype: float
+            path: /config/models/yolov9-c-320.onnx
+            labelmap_path: /labelmap/coco-80.txt
 memryx:
   title: MemryX
   models:

docs/docs/configuration/advanced/reference.md

@@ -152,9 +152,10 @@ auth:
 models:
     # Optional: the camera environment this model is for (default: shown below)
     # Cameras select a model by setting detect -> scene to a matching value, and
-    # a model with a scene of all is used by any camera that does not set one.
+    # the model with a scene of default is used by any camera that does not set one.
-    # Valid values are all, indoor, outdoor, indoor_thermal, outdoor_thermal
+    # Any name made up of letters, numbers, _ and - is valid, such as thermal.
-  - scene: all
+    # Models that use the same model file are combined into one model.
+  - scene: default
     # Required: hardware this model runs on, as <detector> or <detector>:<device>
     # See https://docs.frigate.video/configuration/object_detectors for the
     # detectors available and the devices each one accepts. All of a model's
@@ -293,9 +294,9 @@ ffmpeg:
     # Optional: output args for detect streams (default: shown below)
     detect: -threads 2 -f rawvideo -pix_fmt yuv420p
     # Optional: output args for record streams (default: shown below)
-    record: preset-record-generic
+    record: preset-record-generic-audio-aac
     # Optional: output args for sub stream record streams (default: the record output args above)
-    # record_sub: preset-record-generic
+    # record_sub: preset-record-generic-audio-aac
   # Optional: Time in seconds to wait before ffmpeg retries connecting to the camera. (default: shown below)
   # If set too low, frigate will retry a connection to the camera's stream too frequently, using up the limited streams some cameras can allow at once
   # If set too high, then if a ffmpeg crash or camera stream timeout occurs, you could potentially lose up to a maximum of retry_interval second(s) of footage
@@ -316,9 +317,9 @@ detect:
   # Optional: height of the frame for the input with the detect role (default: use native stream resolution)
   height: 720
   # Optional: the environment this camera looks at, which picks the model it runs on
-  # (default: the model with a scene of all)
+  # (default: the model with a scene of default)
-  # Valid values are all, indoor, outdoor, indoor_thermal, outdoor_thermal
+  # Must match the scene of a configured model
-  scene: outdoor
+  scene: thermal
   # Optional: desired fps for your camera for the input with the detect role (default: shown below)
   # NOTE: Recommended value of 5. Ideally, try and reduce your FPS on the camera.
   fps: 5
@@ -570,6 +571,8 @@ notifications:
   enabled: False
   # Optional: Email for push service to reach out to
   # NOTE: This is required to use notifications
+  # NOTE: Email can be specified with an environment variable or docker secrets that must begin with 'FRIGATE_'.
+  #       e.g. email: '{FRIGATE_NOTIFICATION_EMAIL}'
   email: "admin@example.com"
   # Optional: Cooldown time for notifications in seconds (default: shown below)
   cooldown: 0
@@ -900,6 +903,21 @@ live:
   streams:
     main_stream: main_stream_name
     sub_stream: sub_stream_name
+  # Optional: Lower-quality live streams transcoded by go2rtc while someone is watching.
+  # NOTE: Set at the camera level only.
+  transcode:
+    # Optional: Enable transcoded streams (default: shown below)
+    enabled: False
+    # Optional: go2rtc stream to transcode (default: the first live stream)
+    source: main_stream_name
+    # Optional: One transcoded stream per quality (default: shown below)
+    qualities:
+      - height: 720
+        bitrate: 1200
+      - height: 480
+        bitrate: 500
+      - height: 360
+        bitrate: 250
   # Optional: Set the height of the jsmpeg stream. (default: 720)
   # This must be less than or equal to the height of the detect stream. Lower resolutions
   # reduce bandwidth required for viewing the jsmpeg stream. Width is computed to match known aspect ratio.

docs/docs/configuration/camera_specific.md

@@ -169,10 +169,10 @@ The [Add Camera Wizard](cameras.md#adding-a-camera-with-the-add-camera-wizard) i
 
 1. Click **Add Camera** in <NavPath path="Settings > Global configuration > Camera management" />.
 2. Choose **Manual selection** as the stream detection method and select **Reolink** as the camera brand.
-3. The wizard queries the camera and automatically uses an http-flv stream for cameras 5MP and lower, or an RTSP stream for higher resolution cameras.
+3. The wizard queries the camera and automatically uses an http-flv stream for cameras 5MP and lower. For higher resolution cameras, it tries the http-flv stream first and falls back to RTSP when the camera does not support it.
-4. In the validation step, enable **Use stream compatibility mode** for http-flv streams when the wizard recommends it.
+4. The wizard turns on **Use stream compatibility mode** for the http-flv stream it selects. Enable it for any other http-flv stream you add, such as the sub stream.
 
-If you use the **Probe camera** method instead, the discovered stream URLs will be RTSP. For Reolink cameras where http-flv is recommended, the wizard will show a warning in the validation step.
+If you use the **Probe camera** method instead, the discovered stream URLs will be RTSP. For Reolink cameras 5MP and lower, where http-flv is recommended, the wizard will show a warning in the validation step.
 
 The wizard covers standard single-camera setups. For two way talk, cameras connected through a Reolink NVR, or audio transcoding for WebRTC live view, configure the camera manually as shown below.
 

docs/docs/configuration/custom_classification/state_classification.md

@@ -85,6 +85,14 @@ An optional config, `save_attempts`, can be set as a key under the model name. T
 </TabItem>
 </ConfigTabs>
 
+## Review items
+
+When a model's state changes while its camera has an active review item, the change is recorded on that review item. This includes changes in the few seconds before the item starts, such as a garage door opening just before the car is detected. State changes never create or extend review items on their own, and the first state reported after Frigate starts is not recorded as a change.
+
+Recorded changes appear in the review item's data as `classification_state_changes` (see the [`frigate/reviews`](/integrations/mqtt#frigatereviews) MQTT topic) and are passed to [GenAI review summaries](/configuration/genai/genai_review) as facts, so a description can note that a gate was opened during the activity.
+
+Change times are most accurate with `motion: true`. A model that only runs on an `interval` notices a change at its next run, so the change may be recorded late or attached to a later review item.
+
 ## Training the model
 
 Creating and training the model is done within the Frigate UI using the `Classification` page. The process consists of three steps:

docs/docs/configuration/genai/review_summaries.md

@@ -201,6 +201,8 @@ Review items are sent to the model as a sequence of still frames. Some models fo
 
 The notes come from tracking data rather than from the images, so they describe activity the model may not have picked up on its own. In testing with a person carrying three waste bins to the curb one at a time, `gemma4` described a single trip on every attempt with `frames`, and consistently described multiple trips with `annotated_frames`. Models that already handle these sequences well, such as the `qwen3-vl` family, gain little and should stay on `frames`.
 
+Changes reported by [state classification](/configuration/custom_classification/state_classification#review-items) models during the review item are listed in the prompt in both modes. `annotated_frames` also notes each change before the frame where it happened.
+
 Annotated mode also caps the number of frames, since the notes already establish the order of events and extra near-duplicate frames tend to crowd out the middle of a clip. Longer review items are sampled more sparsely as a result, and typically use fewer tokens than `frames` mode for the same item.
 
 :::note

docs/docs/configuration/license_plate_recognition.md

@@ -379,9 +379,9 @@ Navigate to <NavPath path="Settings > Camera configuration > Object detection" /
 Navigate to <NavPath path="Settings > Camera configuration > Objects" />.
 
 | Field                                                     | Description         |
-| ---------------------------------------------- | ------------------- |
+| --------------------------------------------------------- | ------------------- |
 | **Objects to track**                                      | Add `license_plate` |
-| **Object filters > License Plate > Threshold** | Set to `0.7`        |
+| **Object filters > License Plate > Confidence threshold** | Set to `0.7`        |
 
 Navigate to <NavPath path="Settings > Camera configuration > Motion detection" />.
 

docs/docs/configuration/live.md

@@ -92,7 +92,7 @@ go2rtc:
 
 ### Setting Streams For Live UI
 
-You can configure Frigate to allow manual selection of the stream you want to view in the Live UI. For example, you may want to view your camera's substream on mobile devices, but the full resolution stream on desktop devices. Setting the streams list will populate a dropdown in the UI's Live view that allows you to choose between the streams. This stream setting is _per device_ and is saved in your browser's local storage.
+You can configure Frigate to allow manual selection of the stream you want to view in the Live UI. For example, you may want to view your camera's substream on mobile devices, but the full resolution stream on desktop devices. Setting the streams list will populate a dropdown in the UI's Live view that allows you to choose between the streams. This stream setting is _per device_ and is saved in your browser's local storage. When a camera has more than one stream, the dropdown also offers **Auto**, which is used until you pick a specific stream. Auto starts on the first stream, steps down the list when your connection can't keep up, and steps back up when it recovers. To retry the top stream right away, select **Try highest quality** under the stream picker. List streams from highest to lowest quality, and avoid names that are plain numbers (such as `720`), which the browser sorts ahead of the others. In the UI, drag streams to reorder them, or use **Auto order** to sort them by measured bitrate.
 
 Additionally, when creating and editing camera groups in the UI, you can choose the stream you want to use for your camera group's Live dashboard.
 
@@ -158,6 +158,26 @@ cameras:
 </TabItem>
 </ConfigTabs>
 
+### Transcoded streams
+
+When a camera has no suitable sub stream, Frigate can add lower-quality streams that go2rtc transcodes to H.264 while someone is watching. They appear in the stream list like any other stream, so Auto mode can step down to them. Enable them under <NavPath path="Settings > Camera configuration > Live playback" />, or in YAML:
+
+```yaml
+cameras:
+  test_cam:
+    live:
+      transcode:
+        enabled: true
+        source: test_cam # optional, defaults to the first live stream
+        qualities:
+          - height: 720
+            bitrate: 1200 # kbps
+          - height: 480
+            bitrate: 500
+```
+
+Each quality becomes a go2rtc stream named `<camera>_transcode_<height>p`. go2rtc picks a hardware encoder automatically and falls back to the CPU, which costs CPU for each transcode while it is being watched. Check go2rtc's `api/ffmpeg/hardware` page to see which encoder it found. Using a sub stream as the `source` lowers the cost.
+
 ### WebRTC extra configuration:
 
 WebRTC works by creating a TCP or UDP connection on port `8555`. However, it requires additional configuration:
@@ -363,6 +383,13 @@ When your browser runs into problems playing back your camera streams, it will l
     - `Safari reported InvalidStateError.`
     - `Safari reported decoding errors.`
 
+- **mse-codec**
+  - What it means: go2rtc has no codec for this stream that the browser can play.
+  - What to try: Pick a stream with a codec the browser supports (H.264 is the most compatible), or use a browser that supports the stream's codec. In Auto, Frigate skips this stream for the rest of the session.
+
+  - Possible console messages from the player code:
+    - `mse: streams: codecs not matched: ...`
+
 - **stalled**
   - What it means: Playback has stalled because the player has fallen too far behind live (extended buffering or no data arriving).
   - What to try: This is usually indicative of the browser struggling to decode too many high-resolution streams at once. Try selecting a lower-bandwidth stream (substream), reduce the number of live streams open, improve the network connection, or lower the camera resolution. Also check your camera's keyframe (I-frame) interval: shorter intervals make playback start and recover faster. You can also try increasing the timeout value in <NavPath path="Settings > UI" /> .

docs/docs/configuration/object_detectors.md

@@ -30,6 +30,7 @@ Frigate supports multiple different detectors that work on different types of ha
 
 - [ROCm](#amdrocm-gpu-detector): ROCm can run on AMD Discrete GPUs to provide efficient object detection.
 - [ONNX](#onnx): ROCm will automatically be detected and used as a detector in the `-rocm` Frigate image when a supported ONNX model is configured.
+- <CommunityBadge /> [XDNA2](#amd-xdna2): AMD Ryzen AI / XDNA2 NPUs can run object detection through the community-maintained `frigate-xdna` ZMQ sidecar.
 
 **Apple Silicon**
 
@@ -103,32 +104,36 @@ Coral EdgeTPU and MemryX accelerators can only be opened by one process, so thos
 
 ### Running more than one model
 
-Cameras can be split across models by scene, which is useful when indoor and outdoor cameras benefit from differently trained models. Each model declares the `scene` it is for, and each camera picks one with `detect -> scene`:
+Cameras can be split across models by scene, which is useful when some cameras benefit from a differently trained model, such as thermal cameras. Each model declares the `scene` it is for, and each camera picks one with `detect -> scene`:
 
 ```yaml
 models:
-  - scene: outdoor
+  - scene: default
-    path: plus://your-outdoor-model
+    path: plus://your-model
     devices:
       - edgetpu:pci:0
-  - scene: indoor
+  - scene: thermal
-    path: /config/model_cache/indoor.onnx
+    path: /config/model_cache/thermal.onnx
     model_type: yolo-generic
     devices:
       - openvino:GPU
 
 cameras:
   driveway:
-    detect:
-      scene: outdoor
     ...
-  hallway:
+  backyard_thermal:
     detect:
-      scene: indoor
+      scene: thermal
     ...
 ```
 
-Available scenes are `all`, `indoor`, `outdoor`, `indoor_thermal`, and `outdoor_thermal`. A model with a scene of `all` is used by every camera that does not set one, and `all` is the default when a model does not declare a scene. Changing a camera's scene requires a restart.
+A scene is any name made up of letters, numbers, `_`, and `-`. The model with a scene of `default` is used by every camera that does not set one (or sets a scene that no model is configured for), and `default` is used when a model does not declare a scene. Changing a camera's scene requires a restart.
+
+:::warning
+
+Scenes are for running **different** models. Do not configure the same model under several scenes to dedicate a detector to specific cameras: every detector of a model already serves every camera using it, and splitting them only leaves some detectors idle while others fall behind. Frigate detects models that use the same model file, even under a different path or file name, combines them into one model with all of their hardware, and logs a warning.
+
+:::
 
 ### Choosing a model size
 
@@ -563,6 +568,28 @@ When using CPU detectors, you can add one CPU detector per camera. Adding more d
 
 # Community Supported Detectors
 
+## AMD XDNA2
+
+AMD Ryzen AI / XDNA2 NPUs can be used through the community-maintained
+[frigate-xdna](https://github.com/mitchins/frigate-xdna) detector sidecar.
+The sidecar runs separately from Frigate and connects using Frigate's ZMQ
+detector interface.
+
+Currently qualified on **Ryzen AI Max 300 / Strix Halo**. Other XDNA2 devices
+are not yet qualified; XDNA1 is unsupported.
+
+Follow the frigate-xdna setup instructions to prepare and start the sidecar
+before starting Frigate.
+
+### Configuration {#configuration-xdna2}
+
+Using the detector config below will connect Frigate to the sidecar:
+
+<ModelConfigDropdown detectorTitle="AMD XDNA2" models={objectDetectorsModels.xdna2.models} />
+
+The example assumes Frigate and the sidecar share a Docker network where the
+sidecar is named `xdna`.
+
 ## MemryX MX3
 
 This detector is available for use with the MemryX MX3 accelerator M.2 module. Frigate supports the MX3 on compatible hardware platforms, providing efficient and high-performance object detection.

docs/docs/configuration/object_filters.md

@@ -46,9 +46,9 @@ Any detection below `min_score` will be immediately thrown out and never tracked
 Navigate to <NavPath path="Settings > Global configuration > Objects" /> to set score filters globally.
 
 | Field                                              | Description                                                      |
-| --------------------------------------- | ---------------------------------------------------------------- |
+| -------------------------------------------------- | ---------------------------------------------------------------- |
-| **Object filters > Person > Min Score** | Minimum score for a single detection to initiate tracking        |
+| **Object filters > Person > Minimum confidence**   | Minimum score for a single detection to initiate tracking        |
-| **Object filters > Person > Threshold** | Minimum computed (median) score to be considered a true positive |
+| **Object filters > Person > Confidence threshold** | Minimum computed (median) score to be considered a true positive |
 
 To override score filters for a specific camera, navigate to <NavPath path="Settings > Camera configuration > Objects" /> and select the camera.
 
@@ -104,11 +104,11 @@ Conceptually, a ratio of 1 is a square, 0.5 is a "tall skinny" box, and 2 is a "
 Navigate to <NavPath path="Settings > Global configuration > Objects" /> to set shape filters globally.
 
 | Field                                              | Description                                                              |
-| --------------------------------------- | ------------------------------------------------------------------------ |
+| -------------------------------------------------- | ------------------------------------------------------------------------ |
-| **Object filters > Person > Min Area**  | Minimum bounding box area in pixels (or decimal for percentage of frame) |
+| **Object filters > Person > Minimum object area**  | Minimum bounding box area in pixels (or decimal for percentage of frame) |
-| **Object filters > Person > Max Area**  | Maximum bounding box area in pixels (or decimal for percentage of frame) |
+| **Object filters > Person > Maximum object area**  | Maximum bounding box area in pixels (or decimal for percentage of frame) |
-| **Object filters > Person > Min Ratio** | Minimum width/height ratio of the bounding box                           |
+| **Object filters > Person > Minimum aspect ratio** | Minimum width/height ratio of the bounding box                           |
-| **Object filters > Person > Max Ratio** | Maximum width/height ratio of the bounding box                           |
+| **Object filters > Person > Maximum aspect ratio** | Maximum width/height ratio of the bounding box                           |
 
 To override shape filters for a specific camera, navigate to <NavPath path="Settings > Camera configuration > Objects" /> and select the camera.
 

docs/docs/configuration/objects.md

@@ -71,13 +71,13 @@ Object filters help reduce false positives by constraining the size, shape, and
 Navigate to <NavPath path="Settings > Global configuration > Objects" />.
 
 | Field                                              | Description                                                              |
-| --------------------------------------- | ------------------------------------------------------------------------ |
+| -------------------------------------------------- | ------------------------------------------------------------------------ |
-| **Object filters > Person > Min Area**  | Minimum bounding box area in pixels (or decimal for percentage of frame) |
+| **Object filters > Person > Minimum object area**  | Minimum bounding box area in pixels (or decimal for percentage of frame) |
-| **Object filters > Person > Max Area**  | Maximum bounding box area in pixels (or decimal for percentage of frame) |
+| **Object filters > Person > Maximum object area**  | Maximum bounding box area in pixels (or decimal for percentage of frame) |
-| **Object filters > Person > Min Ratio** | Minimum width/height ratio of the bounding box                           |
+| **Object filters > Person > Minimum aspect ratio** | Minimum width/height ratio of the bounding box                           |
-| **Object filters > Person > Max Ratio** | Maximum width/height ratio of the bounding box                           |
+| **Object filters > Person > Maximum aspect ratio** | Maximum width/height ratio of the bounding box                           |
-| **Object filters > Person > Min Score** | Minimum score for the object to initiate tracking                        |
+| **Object filters > Person > Minimum confidence**   | Minimum score for the object to initiate tracking                        |
-| **Object filters > Person > Threshold** | Minimum computed score to be considered a true positive                  |
+| **Object filters > Person > Confidence threshold** | Minimum computed score to be considered a true positive                  |
 
 To override filters for a specific camera, navigate to <NavPath path="Settings > Camera configuration > Objects" />.
 

docs/docs/configuration/profiles.md

@@ -191,14 +191,12 @@ cameras:
     detect:
       enabled: false
     record:
-      enabled: false
+      enabled: true
     profiles:
       away:
         enabled: true
         detect:
           enabled: true
-        record:
-          enabled: true
       home:
         enabled: false
 ```
@@ -251,6 +249,12 @@ Leaving the `objects` section empty (or omitting `track`) does not clear the lis
 
 Fields that require a Frigate restart to take effect cannot be overridden by profiles, since profiles are applied at runtime without restarting. Those fields are hidden when editing a profile override and can only be changed on the base configuration.
 
+### Why can't a profile enable recording when it's disabled in the base config?
+
+Frigate only sets up a camera's recording stream at startup when recording is enabled in the base config, so enabling it later from a profile has no effect. The same applies to turning recording on from the UI or MQTT.
+
+To keep recording off by default, leave `record.enabled: true` in the base config and create a profile that sets `record.enabled: false`. Activate that profile and it will be restored automatically when Frigate starts.
+
 ### Can I schedule profiles to be enabled or disabled at certain times?
 
 Not within Frigate itself. Frigate is an NVR, not an automation platform, so it intentionally does not include a scheduler for activating profiles. Instead, activate profiles from an automation platform that already handles time- and event-based triggers well, such as [Home Assistant](https://www.home-assistant.io/) or [Node-RED](https://nodered.org/). These integrate with Frigate and give you far more robust and flexible scheduling than a built-in scheduler could.

docs/docs/configuration/semantic_search.md

@@ -245,8 +245,8 @@ Triggers are best configured through the Frigate UI.
 1. Navigate to <NavPath path="Settings > Enrichments > Triggers" /> and select a camera from the dropdown menu.
 2. Click **Add Trigger** to create a new trigger or use the pencil icon to edit an existing one.
 3. In the **Create Trigger** wizard:
-   - Enter a **Name** for the trigger (e.g., "Red Car Alert").
+   - Enter a **Name** for the trigger (e.g., "Red Car Alert"). Frigate derives the trigger's
-   - Enter a descriptive **Friendly Name** for the trigger (e.g., "Red car on the driveway camera").
+     internal **ID** from this name, which can be revealed and edited with the show/hide toggle.
    - Select the **Type** (`Thumbnail` or `Description`).
    - For `Thumbnail`, select an image to trigger this action when a similar thumbnail image is detected, based on the threshold.
    - For `Description`, enter text to trigger this action when a similar tracked object description is detected.

docs/docs/configuration/zones.md

@@ -28,7 +28,7 @@ During testing, enable the Zones option for the [Debug view](/usage/live#the-sin
 1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
 2. Under the **Zones** section, click the plus icon to add a new zone.
 3. Click on the camera's latest image to create the points for the zone boundary. Click the first point again to close the polygon.
-4. Configure zone options such as **Friendly name**, **Objects**, **Loitering time**, and **Inertia** in the zone editor.
+4. Configure zone options such as **Name**, **Objects**, **Loitering Time**, and **Inertia** in the zone editor.
 5. Press **Save** when finished.
 
 </TabItem>
@@ -200,7 +200,7 @@ When using loitering zones, a review item will behave in the following way:
 
 1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
 2. Edit or create the zone (e.g., `sidewalk`).
-   - Set **Loitering time** to the desired number of seconds (e.g., `4`)
+   - Set **Loitering Time** to the desired number of seconds (e.g., `4`)
    - Under **Objects**, add the relevant object types (e.g., `person`)
 
 </TabItem>
@@ -291,7 +291,7 @@ Accurate real-world distance measurements are required to estimate speeds. These
 
 1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
 2. Create or edit a zone with exactly 4 points aligned to the ground plane.
-3. In the zone editor, enter the real-world **Distances** between each pair of consecutive points.
+3. In the zone editor, enable **Speed Estimation** and enter the real-world **Line A distance**, **Line B distance**, **Line C distance**, and **Line D distance** between each pair of consecutive points.
    - For example, if the distance between the first and second points is 10 meters, between the second and third is 12 meters, etc.
 4. Distances are measured in meters (metric) or feet (imperial), depending on the **Unit system** setting.
 
@@ -358,7 +358,7 @@ Zones can be configured with a minimum speed requirement, meaning an object must
 
 1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
 2. Edit or create the zone with distances configured.
-   - Set **Speed threshold** to the desired minimum speed (e.g., `20`)
+   - Set **Speed Threshold** to the desired minimum speed (e.g., `20`)
    - The unit is kph or mph, depending on the **Unit system** setting
 
 </TabItem>

docs/docs/frigate/glossary.md

@@ -54,7 +54,7 @@ An object filter mask drops any [bounding box](#bounding-box) whose bottom cente
 
 ## Min Score
 
-The lowest score a detected object can have to be kept during tracking. Anything scoring below the minimum is assumed to be a [false positive](#false-positive) and discarded.
+The lowest score a detected object can have to be kept during tracking. Anything scoring below the minimum is assumed to be a [false positive](#false-positive) and discarded. Set with `min_score` in the config, shown as **Minimum confidence** in the settings UI.
 
 ## Model
 
@@ -86,7 +86,7 @@ A more specific identity assigned to a [tracked object](#tracked-object-event-in
 
 ## Threshold
 
-The median score an object must reach to be considered a true positive.
+The median score an object must reach to be considered a true positive. Set with `threshold` in the config, shown as **Confidence threshold** in the settings UI.
 
 ## Top Score
 

docs/docs/frigate/hardware.md

@@ -75,6 +75,9 @@ Frigate supports multiple different detectors that work on different types of ha
 - [ROCm](#rocm---amd-gpu): ROCm can run on AMD Discrete GPUs to provide efficient object detection
   - [Supports limited model architectures](../../configuration/object_detectors#amdrocm-gpu-detector)
   - Runs best on discrete AMD GPUs
+- <CommunityBadge /> [XDNA2 (Ryzen AI)](#amd-xdna2): AMD XDNA2 NPU (sub-watt power AI/ML processor separate to the GPU) inside Strix and other "AI" branded AMD platforms
+  - Has only been tested with YOLOv9, in theory other graphs may be compiled too.
+  - Runs via ZMQ proxy which adds some latency, only recommended for local connection
 
 **Apple Silicon**
 
@@ -186,7 +189,7 @@ Frigate is able to utilize an Nvidia GPU which supports the 12.x series of CUDA
 
 #### Minimum Hardware Support
 
-12.x series of CUDA libraries are used which have minor version compatibility. The minimum driver version on the host system must be `>=545`. Also the GPU must support a Compute Capability of `5.0` or greater. This generally correlates to a Maxwell-era GPU or newer, check the NVIDIA GPU Compute Capability table linked below.
+12.x series of CUDA libraries are used which have minor version compatibility. The minimum driver version on the host system must be `>=545`. The oldest supported GPU generation for object detection is Pascal (Compute Capability 6.0 or greater). Older generations such as Maxwell are not supported but can still be used by following a [community workaround](https://github.com/blakeblackshear/frigate/discussions/20088). For other GPUs, check the NVIDIA GPU Compute Capability table linked below.
 
 Make sure your host system has the [nvidia-container-runtime](https://docs.docker.com/config/containers/resource_constraints/#access-an-nvidia-gpu) installed to pass through the GPU to the container and the host system has a compatible driver installed for your GPU.
 
@@ -338,6 +341,32 @@ The inference time of a rk3588 with all 3 cores enabled is typically 25-30 ms fo
 | ---------------- | ----------------------------------- |
 | yolov9-tiny      | ~ 4 ms                              |
 
+### AMD Ryzen AI / XDNA2
+
+Frigate supports AMD XDNA2 NPUs through the community-maintained
+frigate-xdna ZMQ sidecar. It works with stock Frigate and supports
+Frigate+ models or compatible local YOLO ONNX models. Models are compiled
+once on the target system and cached for subsequent use.
+
+Currently qualified on **Ryzen AI Max 300 / Strix Halo**. Other XDNA2
+devices are not yet qualified; XDNA1 is unsupported.
+
+Measured YOLOv9 detector latency on Strix Halo:
+
+| Model | 320 | 640 |
+| ----- | ---: | ---: |
+| YOLOv9-T | ~7.4 ms | unsupported |
+| YOLOv9-S | ~9.0 ms | ~20.0 ms |
+| YOLOv9-M | ~13.1 ms | ~34.4 ms |
+| YOLOv9-C | ~14.1 ms | ~35.2 ms |
+| YOLOv9-E | ~69.4 ms | ~224.8 ms |
+
+**YOLOv9-C at 320 is the recommended quality/performance balance.**
+C at 640 is also usable where the lower throughput is acceptable.
+
+Setup, model preparation, and compatibility details are available
+[in the frigate-xdna documentation](https://github.com/mitchins/frigate-xdna).
+
 ## What does Frigate use the CPU for and what does it use a detector for? (ELI5 Version)
 
 This is taken from a [user question on reddit](https://www.reddit.com/r/homeassistant/comments/q8mgau/comment/hgqbxh5/?utm_source=share&utm_medium=web2x&context=3). Modified slightly for clarity.

docs/docs/integrations/mqtt.md

@@ -11,6 +11,12 @@ MQTT requires a network connection to your broker. This is typically local, but
 
 :::
 
+:::note
+
+Wherever a topic below includes a camera, mask, or zone name, use its `ID` from the config, not its `friendly_name`. For example, a camera with `friendly_name: "Back Yard"` and ID `back_yard` publishes to `frigate/back_yard/...`, not `frigate/Back Yard/...`.
+
+:::
+
 ## General Frigate Topics
 
 ### `frigate/available`
@@ -212,6 +218,7 @@ An `update` with the same ID will be published when:
 - The severity changes from `detection` to `alert`
 - Additional objects are detected
 - An object is recognized via face, lpr, etc.
+- A [state classification](/configuration/custom_classification/state_classification#review-items) model changes state
 
 When the review activity has ended a final `end` message is published.
 
@@ -235,7 +242,8 @@ When the review activity has ended a final `end` message is published.
       "objects": ["person", "car"],
       "sub_labels": [],
       "zones": [],
-      "audio": []
+      "audio": [],
+      "classification_state_changes": []
     }
   },
   "after": {
@@ -254,7 +262,16 @@ When the review activity has ended a final `end` message is published.
       "objects": ["person", "car"],
       "sub_labels": ["Bob"],
       "zones": ["front_yard"],
-      "audio": []
+      "audio": [],
+      "classification_state_changes": [
+        // verified changes of state classification models on this camera
+        {
+          "model": "front_gate",
+          "from": "closed",
+          "to": "open",
+          "timestamp": 1718987131.52
+        }
+      ]
     }
   }
 }

docs/docs/integrations/third_party_extensions.md

@@ -27,6 +27,10 @@ The [Advanced Camera Card](https://card.camera/#/README) is a Home Assistant das
 It supports automatically setting the sub labels in Frigate for person objects that are detected and recognized.
 This is a fork (with fixed errors and new features) of [original Double Take](https://github.com/jakowenko/double-take) project which, unfortunately, isn't being maintained by author.
 
+## [frigate-abr](https://github.com/007hacky007/frigate-abr)
+
+[frigate-abr](https://github.com/007hacky007/frigate-abr) is a drop-in Docker image of Frigate that adds adaptive bitrate (ABR) playback for recordings: a sidecar transcodes footage to lower quality tiers on demand, for reviewing over slow remote connections. Segments are transcoded when played and cached, so no additional stream is recorded. Frigate itself is not modified.
+
 ## [Frigate Notify](https://github.com/0x2142/frigate-notify)
 
 [Frigate Notify](https://github.com/0x2142/frigate-notify) is a simple app designed to send notifications from Frigate to your favorite platforms. Intended to be used with standalone Frigate installations - Home Assistant not required, MQTT is optional but recommended.

docs/docs/plus/faq.md

@@ -21,7 +21,13 @@ Yes. Models and metadata are stored in the `model_cache` directory within the co
 
 ### Can I keep using my Frigate+ models even if I do not renew my subscription?
 
-Yes. Subscriptions to Frigate+ provide access to the infrastructure used to train the models. Models trained with your subscription are yours to keep and use forever. However, do note that the terms and conditions prohibit you from sharing, reselling, or creating derivative products from the models.
+Yes. Subscriptions to Frigate+ provide access to the infrastructure used to train the models. Models you train during an active subscription remain licensed for your continued use even after your subscription ends — models already in your model cache will keep working indefinitely. An active subscription is required to train new models and download new versions.
+
+### Can I use Frigate+ models commercially?
+
+A standard subscription covers use on camera systems you own or operate, including for your business. A shop, restaurant, warehouse, or office running Frigate+ at its own locations (including multiple locations) is exactly the kind of use the subscription is for.
+What the standard subscription does not cover is using Frigate+ models to provide a product or service to others. If you're deploying models at your customers' sites, bundling them with hardware you sell, or running them as part of a hosted or managed service, even if your customers never receive the model files themselves, you'll need a commercial license.
+Note that professional installers are fine under standard subscriptions when each customer holds their own Frigate+ subscription. The commercial license is for cases where your license powers your customers' sites.
 
 ### Why can't I submit images to Frigate+?
 

docs/docs/plus/first_model.md

@@ -63,10 +63,10 @@ Frigate+ models generally have much higher scores than the default model provide
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > Global configuration > Objects" />. Under **Object filters**, set **Min Score** and **Threshold** for each object type, then click **Save**.
+Navigate to <NavPath path="Settings > Global configuration > Objects" />. Under **Object filters**, set **Minimum confidence** and **Confidence threshold** for each object type, then click **Save**.
 
-| Object            | Min Score | Threshold |
+| Object            | Minimum confidence | Confidence threshold |
-| ----------------- | --------- | --------- |
+| ----------------- | ------------------ | -------------------- |
 | **dog**           | .7                 | .9                   |
 | **cat**           | .65                | .8                   |
 | **face**          | .7                 |                      |

docs/docs/plus/index.md

@@ -65,11 +65,11 @@ Some users may find that Frigate+ models result in more false positives initiall
 
 Frigate+ models support a more relevant set of objects for security cameras. The labels for annotation in Frigate+ are configurable by editing the camera in the Cameras section of Frigate+. Currently, the following objects are supported:
 
-- **People**: `person`, `face`
+- **People**: `person`, `face`, `baby`
-- **Vehicles**: `car`, `motorcycle`, `bicycle`, `boat`, `school_bus`, `license_plate`
+- **Vehicles**: `car`, `motorcycle`, `bicycle`, `boat`, `school_bus`, `garbage truck`, `license_plate`
 - **Delivery Logos**: `amazon`, `usps`, `ups`, `fedex`, `dhl`, `an_post`, `purolator`, `postnl`, `nzpost`, `postnord`, `gls`, `dpd`, `canada_post`, `royal_mail`
-- **Animals**: `dog`, `cat`, `deer`, `horse`, `bird`, `raccoon`, `fox`, `bear`, `cow`, `squirrel`, `goat`, `rabbit`, `skunk`, `kangaroo`
+- **Animals**: `dog`, `cat`, `deer`, `horse`, `bird`, `raccoon`, `fox`, `bear`, `cow`, `squirrel`, `goat`, `rabbit`, `skunk`, `kangaroo`, `possum`, `rodent`
-- **Other**: `package`, `waste_bin`, `bbq_grill`, `robot_lawnmower`, `umbrella`
+- **Other**: `package`, `waste_bin`, `bbq_grill`, `robot_lawnmower`, `umbrella`, `baby_stroller`
 
 Other object types available in the default Frigate model are not available. Additional object types will be added in future releases.
 
@@ -77,9 +77,12 @@ Other object types available in the default Frigate model are not available. Add
 
 Candidate labels are also available for annotation. These labels don't have enough data to be included in the model yet, but using them will help add support sooner. You can enable these labels by editing the camera settings.
 
-Where possible, these labels are mapped to existing labels during training. For example, any `baby` labels are mapped to `person` until support for new labels is added.
+Where possible, these labels are mapped to existing labels during training. For example, any `duck` labels are mapped to `bird` until support for new labels is added.
 
-The candidate labels are: `baby`, `bpost`, `badger`, `possum`, `rodent`, `chicken`, `groundhog`, `boar`, `hedgehog`, `tractor`, `golf cart`, `garbage truck`, `bus`, `sports ball`, `la_poste`, `lawnmower`, `heron`, `rickshaw`, `wombat`, `auspost`, `aramex`, `bobcat`, `mustelid`, `transoflex`, `airplane`, `drone`, `mountain_lion`, `crocodile`, `turkey`, `baby_stroller`, `monkey`, `coyote`, `porcupine`, `parcelforce`, `sheep`, `snake`, `helicopter`, `lizard`, `duck`, `hermes`, `cargus`, `fan_courier`, `sameday`
+- **Vehicles**: `tractor`, `golf_cart`, `bus`, `airplane`, `helicopter`, `rickshaw`, `scooter`
+- **Delivery Logos**: `bpost`, `auspost`, `aramex`, `transoflex`, `parcelforce`, `hermes`, `cargus`, `fan_courier`, `sameday`, `la_poste`
+- **Animals**: `badger`, `chicken`, `duck`, `turkey`, `groundhog`, `boar`, `hedgehog`, `wombat`, `bobcat`, `mustelid`, `mountain_lion`, `crocodile`, `monkey`, `coyote`, `porcupine`, `sheep`, `snake`, `lizard`, `heron`, `elk`, `moose`, `pig`, `donkey`, `civet`
+- **Other**: `sports_ball`, `drone`, `lawnmower`
 
 Candidate labels are not available for automatic suggestions.
 

docs/docs/troubleshooting/recordings.md

@@ -397,19 +397,11 @@ dmesg | grep -i -E "gpu|drm|reset|hang"
 
 Messages like `trying reset from guc_exec_queue_timedout_job` or similar GPU reset/hang messages indicate a driver or hardware issue. Ensure your kernel and GPU drivers (especially Intel) are up to date.
 
-#### Step 6: Verify hardware acceleration configuration
+#### Step 6: Verify go2rtc stream configuration
-
-An incorrect `hwaccel_args` preset can cause ffmpeg to fail silently or consume excessive CPU, starving the detector of resources.
-
-- After upgrading Frigate, verify your preset matches your hardware (e.g., `preset-intel-qsv-h264` instead of the deprecated `preset-vaapi`).
-- For h265 cameras, use the corresponding h265 preset (e.g., `preset-intel-qsv-h265`).
-- Note that `hwaccel_args` are only relevant for the detect stream. Frigate does not decode the record stream.
-
-#### Step 7: Verify go2rtc stream configuration
 
 Ensure that the ffmpeg source names in your go2rtc configuration match the correct camera stream. A misconfigured stream name (e.g., copying a config from one camera to another without updating the stream reference) will cause the wrong stream to be used or the stream to fail entirely.
 
-#### Step 8: Check system resources
+#### Step 7: Check system resources
 
 If none of the above apply, the issue may be a general resource constraint. Monitor the following on your host:
 

docs/docusaurus.config.ts

@@ -3,6 +3,9 @@ import * as path from "node:path";
 import type { Config, PluginConfig } from "@docusaurus/types";
 import type * as OpenApiPlugin from "docusaurus-plugin-openapi-docs";
 
+// Bump when a new stable release ships
+const STABLE_VERSION = "0.18";
+
 const config: Config = {
   title: "Frigate",
   tagline: "NVR With Realtime Object Detection for IP Cameras",
@@ -23,17 +26,17 @@ const config: Config = {
     mermaid: true,
   },
   i18n: {
-    defaultLocale: 'en',
+    defaultLocale: "en",
-    locales: ['en'],
+    locales: ["en"],
     localeConfigs: {
       en: {
-        label: 'English',
+        label: "English",
-      }
+      },
     },
   },
   themeConfig: {
     announcementBar: {
-      id: 'frigate_plus',
+      id: "frigate_plus",
       content: `
         <span style="margin-right: 8px; display: inline-block; animation: pulse 2s infinite;">🚀</span>
         Get more relevant and accurate detections with Frigate+ models.
@@ -45,8 +48,8 @@ const config: Config = {
             50%       { transform: scale(1.1); }
           }
         </style>`,
-      backgroundColor: '#005f73',
+      backgroundColor: "#005f73",
-      textColor: '#e0fbfc',
+      textColor: "#e0fbfc",
       isCloseable: false,
     },
     docs: {
@@ -85,13 +88,13 @@ const config: Config = {
     prism: {
       magicComments: [
         {
-          className: 'theme-code-block-highlighted-line',
+          className: "theme-code-block-highlighted-line",
-          line: 'highlight-next-line',
+          line: "highlight-next-line",
-          block: {start: 'highlight-start', end: 'highlight-end'},
+          block: { start: "highlight-start", end: "highlight-end" },
         },
         {
-          className: 'code-block-error-line',
+          className: "code-block-error-line",
-          line: 'highlight-error-line',
+          line: "highlight-error-line",
         },
       ],
       additionalLanguages: ["bash", "json"],
@@ -131,6 +134,11 @@ const config: Config = {
         srcDark: "img/branding/logo-dark.svg",
       },
       items: [
+        {
+          href: "https://github.com/blakeblackshear/frigate/releases",
+          label: `${STABLE_VERSION}`,
+          position: "left",
+        },
         {
           to: "/",
           activeBasePath: "docs",
@@ -148,19 +156,19 @@ const config: Config = {
           position: "right",
         },
         {
-          type: 'localeDropdown',
+          type: "localeDropdown",
-          position: 'right',
+          position: "right",
           dropdownItemsAfter: [
             {
-              label: '简体中文（社区翻译）',
+              label: "简体中文（社区翻译）",
-              href: 'https://docs.frigate-cn.video',
+              href: "https://docs.frigate-cn.video",
-            }
+            },
-          ]
+          ],
         },
         {
-          href: 'https://github.com/blakeblackshear/frigate',
+          href: "https://github.com/blakeblackshear/frigate",
-          label: 'GitHub',
+          label: "GitHub",
-          position: 'right',
+          position: "right",
         },
       ],
     },

docs/package-lock.json

@@ -7030,9 +7030,9 @@
       }
     },
     "node_modules/brace-expansion": {
-      "version": "1.1.18",
+      "version": "1.1.21",
-      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-1.1.18.tgz",
+      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-1.1.21.tgz",
-      "integrity": "sha512-Edep/X9fGqVNmzKBVsDYIOtD+z1tuezV70LBjdCst9Tqu76lsnvRiZ6oTic1n+/BIwX6QDGAO94PN4N2SADvtw==",
+      "integrity": "sha512-9zeA+KLZNNzglF2TPKRQEDyx6Yby7daAkuy8MiPzpXPsYDWi/DRM8jmwUDxokQjYqBpv5DgPiwD4h4ZZSy1Ujw==",
       "license": "MIT",
       "dependencies": {
         "balanced-match": "^1.0.0",
@@ -7698,14 +7698,15 @@
       }
     },
     "node_modules/compression": {
-      "version": "1.8.1",
+      "version": "1.8.2",
-      "resolved": "https://registry.npmjs.org/compression/-/compression-1.8.1.tgz",
+      "resolved": "https://registry.npmjs.org/compression/-/compression-1.8.2.tgz",
-      "integrity": "sha512-9mAqGPHLakhCLeNyxPkK4xVo746zQ/czLH1Ky+vkitMnWfWZps8r0qXuwhwizagCRttsL4lfG4pIOvaWLpAP0w==",
+      "integrity": "sha512-o8vI5RE5A6EVVOd9o41jKp41aJom+QTEO/Bx8MYNjexMo/Bv2WOjUfZr+aL0WnYSgymUy6zeguqLTsIhV0gMvQ==",
       "license": "MIT",
       "dependencies": {
         "bytes": "3.1.2",
         "compressible": "~2.0.18",
         "debug": "2.6.9",
+        "destroy": "1.2.0",
         "negotiator": "~0.6.4",
         "on-headers": "~1.1.0",
         "safe-buffer": "5.2.1",
@@ -7713,6 +7714,10 @@
       },
       "engines": {
         "node": ">= 0.8.0"
+      },
+      "funding": {
+        "type": "opencollective",
+        "url": "https://opencollective.com/express"
       }
     },
     "node_modules/compression/node_modules/bytes": {
@@ -9460,9 +9465,9 @@
       }
     },
     "node_modules/dompurify": {
-      "version": "3.4.13",
+      "version": "3.4.16",
-      "resolved": "https://registry.npmjs.org/dompurify/-/dompurify-3.4.13.tgz",
+      "resolved": "https://registry.npmjs.org/dompurify/-/dompurify-3.4.16.tgz",
-      "integrity": "sha512-2vmYIoqjze2d+kakP8S/nS5shfsl587kzwEjcGlTdiksUVgFHnFCsLYDVj/JNqJVOQZGSYBTmuycv0PodwmnMQ==",
+      "integrity": "sha512-sqo+pNp3qRhCIpbgRi1y8Tgk27Bo2Ry7w0dC1NBeNTdZChWjz9Xb/KOoZbRP/R6pQZ80Qw8YhXw13hWWBbMRnQ==",
       "license": "(MPL-2.0 OR Apache-2.0)",
       "optionalDependencies": {
         "@types/trusted-types": "^2.0.7"
@@ -10125,9 +10130,9 @@
       "license": "MIT"
     },
     "node_modules/fast-uri": {
-      "version": "3.1.7",
+      "version": "3.1.8",
-      "resolved": "https://registry.npmjs.org/fast-uri/-/fast-uri-3.1.7.tgz",
+      "resolved": "https://registry.npmjs.org/fast-uri/-/fast-uri-3.1.8.tgz",
-      "integrity": "sha512-dOvZVzjdZdz7phd9v6jCbwxrBW3fK6n8Rc0CtdmM4bumzMnxywBYhuph6J819RRw/ku+rLbelwfMunktuzVVHg==",
+      "integrity": "sha512-GZMtZUTNRpOVIECoXwLNZS5xUGE+mVNbTB8h/7Rwh2TFWcBQiPzTgyZi05BF9UMZKkLJv8XBRJTlU7zg8+ZfMg==",
       "funding": [
         {
           "type": "github",
@@ -17448,9 +17453,9 @@
       "license": "ISC"
     },
     "node_modules/proxy-addr": {
-      "version": "2.0.7",
+      "version": "2.0.8",
-      "resolved": "https://registry.npmjs.org/proxy-addr/-/proxy-addr-2.0.7.tgz",
+      "resolved": "https://registry.npmjs.org/proxy-addr/-/proxy-addr-2.0.8.tgz",
-      "integrity": "sha512-llQsMLSUDUPT44jdrU/O37qlnifitDP+ZwrmmZcoSKyLKvtZxpyV0n2/bD/N4tBAAZ/gJEdZU7KMraoK1+XYAg==",
+      "integrity": "sha512-5nnx0yGyVUcY6t9RnWcARWtwT9F1D8O9rt08htPvnd49W1IgZtmLkhu9WfMzQj1cFxjHIO6connUNVW5k7AVyQ==",
       "license": "MIT",
       "dependencies": {
         "forwarded": "0.2.0",
@@ -17458,6 +17463,10 @@
       },
       "engines": {
         "node": ">= 0.10"
+      },
+      "funding": {
+        "type": "opencollective",
+        "url": "https://opencollective.com/express"
       }
     },
     "node_modules/proxy-addr/node_modules/ipaddr.js": {
@@ -19229,9 +19238,9 @@
       }
     },
     "node_modules/shell-quote": {
-      "version": "1.10.0",
+      "version": "1.12.0",
-      "resolved": "https://registry.npmjs.org/shell-quote/-/shell-quote-1.10.0.tgz",
+      "resolved": "https://registry.npmjs.org/shell-quote/-/shell-quote-1.12.0.tgz",
-      "integrity": "sha512-w1aiOKwKuRgtwAReIIj89puqg+I7GvX4IbLrvmhXbzQsj1+Zwi4VO3+fa6ZF91TWSjIxoEkKnMeHcLEODK5ZXA==",
+      "integrity": "sha512-PcByqNyT/38F2kDNi006HAMRJaULuBzq/FOsw3qdZvX/GA9W/jamDaRskgHjubHiftXK5sIFxLNkvrXUwcof6Q==",
       "license": "MIT",
       "engines": {
         "node": ">= 0.4"
@@ -19513,9 +19522,9 @@
       }
     },
     "node_modules/source-map-js": {
-      "version": "1.2.1",
+      "version": "1.2.2",
-      "resolved": "https://registry.npmjs.org/source-map-js/-/source-map-js-1.2.1.tgz",
+      "resolved": "https://registry.npmjs.org/source-map-js/-/source-map-js-1.2.2.tgz",
-      "integrity": "sha512-UXWMKhLOwVKb728IUtQPXxfYU+usdybtUrK/8uGE8CQMvrhOpwvzDBwj0QhSL7MQc7vIsISBG8VQ8+IDQxpfQA==",
+      "integrity": "sha512-KGj/8Y43x35aZVDtt+J4mK1hoLGHULMYfSkODJNQjNDC3oW1PqPoxMwo0pLUsWM/UEGzON/NxeHywEfNXNP3Vw==",
       "license": "BSD-3-Clause",
       "engines": {
         "node": ">=0.10.0"

docs/static/frigate-api.yaml

@@ -412,6 +412,39 @@ paths:
       security:
         - frigateAdminAuth: []
       x-required-role: admin
+  /go2rtc/streams/{stream_name}/bitrate:
+    get:
+      tags:
+        - Camera
+      summary: Go2Rtc Stream Bitrate
+      description: |-
+        **Access:** Admin role required.
+
+        Measure a go2rtc stream's bitrate over a few seconds.
+      operationId: 
+        go2rtc_stream_bitrate_go2rtc_streams__stream_name__bitrate_get
+      parameters:
+        - name: stream_name
+          in: path
+          required: true
+          schema:
+            type: string
+            title: Stream Name
+      responses:
+        '200':
+          description: Successful Response
+          content:
+            application/json:
+              schema: {}
+        '422':
+          description: Validation Error
+          content:
+            application/json:
+              schema:
+                $ref: '#/components/schemas/HTTPValidationError'
+      security:
+        - frigateAdminAuth: []
+      x-required-role: admin
   /ffprobe:
     get:
       tags:
@@ -4460,6 +4493,16 @@ paths:
               - type: 'null'
             default: 100
             title: Limit
+        - name: offset
+          in: query
+          required: false
+          schema:
+            anyOf:
+              - type: integer
+                minimum: 0
+              - type: 'null'
+            default: 0
+            title: Offset
         - name: after
           in: query
           required: false
@@ -4765,6 +4808,16 @@ paths:
               - type: 'null'
             default: 50
             title: Limit
+        - name: offset
+          in: query
+          required: false
+          schema:
+            anyOf:
+              - type: integer
+                minimum: 0
+              - type: 'null'
+            default: 0
+            title: Offset
         - name: cameras
           in: query
           required: false
@@ -7731,6 +7784,11 @@ components:
           type: boolean
           title: Skip Save
           default: false
+        replace_paths:
+          items:
+            type: string
+          type: array
+          title: Replace Paths
       type: object
       title: AppConfigSetBody
     AppPostLoginBody:
@@ -7959,7 +8017,7 @@ components:
       properties:
         file:
           type: string
-          format: binary
+          contentMediaType: application/octet-stream
           title: File
       type: object
       required:
@@ -7969,7 +8027,7 @@ components:
       properties:
         file:
           type: string
-          format: binary
+          contentMediaType: application/octet-stream
           title: File
       type: object
       required:
@@ -9543,6 +9601,11 @@ components:
         type:
           type: string
           title: Error Type
+        input:
+          title: Input
+        ctx:
+          type: object
+          title: Context
       type: object
       required:
         - loc

frigate/api/app.py

@@ -63,6 +63,7 @@ from frigate.util.builtin import (
     flatten_config_data,
     load_labels,
     process_config_query_string,
+    split_config_key_path,
     update_yaml_file_bulk,
 )
 from frigate.util.config import (
@@ -70,6 +71,10 @@ from frigate.util.config import (
     find_config_file,
     redact_credential,
 )
+from frigate.util.live_streams import (
+    generated_transcode_streams,
+    sync_transcode_streams,
+)
 from frigate.util.object_names import get_categorized_object_names
 from frigate.util.schema import get_config_schema
 from frigate.util.services import (
@@ -306,9 +311,12 @@ def config(request: Request):
         mode="json", warnings="none", exclude_none=True
     )
 
-    # remove environment_vars for non-admin users
+    is_admin = request.headers.get("remote-role") == "admin"
-    if request.headers.get("remote-role") != "admin":
+
+    # hide environment_vars and the notification email from non-admin users
+    if not is_admin:
         config.pop("environment_vars", None)
+        redact_credential(config["notifications"], "email")
 
     # redact mqtt credentials
     redact_credential(config["mqtt"], "password")
@@ -365,7 +373,15 @@ def config(request: Request):
                 camera_name
             )
             if base_sections:
-                camera_dict["base_config"] = base_sections
+                # copy so redaction below can't alter the profile manager's cache
+                camera_dict["base_config"] = copy.deepcopy(base_sections)
+
+        # cameras inherit the global notification email
+        if not is_admin:
+            redact_credential(camera_dict["notifications"], "email")
+            redact_credential(
+                camera_dict.get("base_config", {}).get("notifications", {}), "email"
+            )
 
     # remove go2rtc stream passwords
     go2rtc: dict[str, Any] = config_obj.go2rtc.model_dump(
@@ -393,6 +409,11 @@ def config(request: Request):
         model_dict["non_logo_attributes"] = model.non_logo_attributes
         model_dict["labelmap"] = model.merged_labelmap
 
+        # report the configured reference rather than the resolved cache path,
+        # so saving the config back doesn't lose the Frigate+ model
+        if model.plus_id:
+            model_dict["path"] = f"plus://{model.plus_id}"
+
         if not config["plus"]["enabled"]:
             continue
 
@@ -810,6 +831,17 @@ def _config_set_in_memory(request: Request, body: AppConfigSetBody) -> JSONRespo
         )
 
 
+def _config_path_exists(data: Any, key_path: str) -> bool:
+    """Return whether a dotted config path is present in parsed yaml."""
+    for key in split_config_key_path(key_path):
+        if not isinstance(data, dict) or key not in data:
+            return False
+
+        data = data[key]
+
+    return True
+
+
 @router.put("/config/set", dependencies=[Depends(require_role(["admin"]))])
 def config_set(request: Request, body: AppConfigSetBody):
     config_file = find_config_file()
@@ -864,6 +896,19 @@ def config_set(request: Request, body: AppConfigSetBody):
                         status_code=400,
                     )
 
+                # delete replaced paths first so their maps are rewritten in
+                # the order sent; update_yaml would otherwise keep old order
+                if body.replace_paths:
+                    old_yaml = ruamel.yaml.YAML(typ="safe").load(old_raw_config) or {}
+                    updates = {
+                        **{
+                            path: ""
+                            for path in body.replace_paths
+                            if _config_path_exists(old_yaml, path)
+                        },
+                        **updates,
+                    }
+
                 # apply all updates in a single operation
                 update_yaml_file_bulk(config_file, updates)
 
@@ -927,9 +972,15 @@ def config_set(request: Request, body: AppConfigSetBody):
             if request.app.dispatcher is not None:
                 request.app.dispatcher.clear_runtime_state_for_yaml_keys(updates.keys())
 
+            go2rtc_synced = True
+
             if body.requires_restart == 0 or body.update_topic:
                 old_config: FrigateConfig = request.app.frigate_config
                 swap_runtime_config(request.app, config)
+                go2rtc_synced = sync_transcode_streams(
+                    generated_transcode_streams(old_config),
+                    generated_transcode_streams(config),
+                )
 
                 if body.update_topic:
                     if body.update_topic.startswith("config/cameras/"):
@@ -989,6 +1040,7 @@ def config_set(request: Request, body: AppConfigSetBody):
                             if body.requires_restart == 0
                             else "Config successfully updated, restart to apply"
                         ),
+                        "go2rtc_synced": go2rtc_synced,
                     }
                 ),
                 status_code=200,

frigate/api/auth.py

@@ -130,23 +130,18 @@ def require_admin_by_default():
         if path.startswith(EXEMPT_PREFIXES):
             return
 
-        # Dynamic camera path exemption:
+        # Camera routes enforce per-camera access via route-level dependencies
-        # Any path whose first segment matches a configured camera name should
+        # (e.g. require_camera_access). Match on the route template, not the raw
-        # bypass the global admin requirement. These endpoints enforce access
+        # path, so a camera named like another namespace (e.g. "faces") can't
-        # via route-level dependencies (e.g. require_camera_access) to ensure
+        # waive the admin check for that namespace's routes.
-        # per-camera authorization. This allows non-admin authenticated users
+        route = request.scope.get("route")
-        # (e.g. viewer role) to access camera-specific resources without
-        # needing admin privileges.
-        try:
-            if path.startswith("/"):
-                first_segment = path.split("/", 2)[1]
         if (
-                    first_segment
+            route is not None
-                    and first_segment in request.app.frigate_config.cameras
+            and route.path.startswith("/{camera_name}")
+            and request.path_params.get("camera_name")
+            in request.app.frigate_config.cameras
         ):
             return
-        except Exception:
-            pass
 
         # For all other paths, require admin role
         # Internal port requests have admin role set automatically
@@ -324,11 +319,17 @@ def get_remote_addr(request: Request):
             network = ipaddress.ip_network(proxy)
         except ValueError:
             logger.warning(f"Unable to parse trusted network: {proxy}")
+            continue
         trusted_proxies.append(network)
 
     # return the first remote address that is not trusted
     for addr in route:
+        try:
             ip = ipaddress.ip_address(addr.strip())
+        except ValueError:
+            logger.debug("Invalid address in X-Forwarded-For header")
+            return direct_addr or "127.0.0.1"
+
         logger.debug(f"Checking {ip} (v{ip.version})")
         trusted = False
         for trusted_proxy in trusted_proxies:
@@ -473,12 +474,11 @@ def create_encoded_jwt(user, role, expiration, secret):
 
 def set_jwt_cookie(response: Response, cookie_name, encoded_jwt, max_age, secure):
     # TODO: ideally this would set secure as well, but that requires TLS
-    # SameSite is intentionally left unset (browsers default to Lax). Setting
+    # Starlette sets SameSite=Lax by default. The cookie is still sent to
-    # SameSite=Lax/Strict would stop the cookie from being sent in cross-origin
+    # same-site iframes (e.g. Home Assistant on the same host or domain), but
-    # iframes, breaking embedded views such as the Home Assistant Frigate card.
+    # not to cross-site ones. CSRF is also mitigated by requiring a custom
-    # CSRF is instead mitigated by requiring a custom X-CSRF-TOKEN header, which
+    # X-CSRF-TOKEN header, which cross-origin pages cannot set without a CORS
-    # cross-origin pages cannot set without a CORS preflight that Frigate never
+    # preflight that Frigate never grants (see check_csrf in api/fastapi_app.py).
-    # grants (see check_csrf in api/fastapi_app.py).
     response.set_cookie(
         key=cookie_name,
         value=encoded_jwt,

frigate/api/camera.py

@@ -39,6 +39,11 @@ from frigate.util.builtin import clean_camera_user_pass, get_record_segment_time
 from frigate.util.camera_cleanup import cleanup_camera_db, cleanup_camera_files
 from frigate.util.config import find_config_file
 from frigate.util.image import run_ffmpeg_snapshot
+from frigate.util.live_streams import (
+    generated_transcode_streams,
+    measure_stream_bitrate,
+    sync_transcode_streams,
+)
 from frigate.util.services import (
     analyze_record_keyframes,
     ffprobe_stream,
@@ -248,6 +253,34 @@ def go2rtc_delete_stream(stream_name: str):
         )
 
 
+@router.get(
+    "/go2rtc/streams/{stream_name}/bitrate",
+    dependencies=[Depends(require_role(["admin"]))],
+)
+async def go2rtc_stream_bitrate(request: Request, stream_name: str):
+    """Measure a go2rtc stream's bitrate over a few seconds."""
+    config: FrigateConfig = request.app.frigate_config
+    known = set(config.go2rtc.model_dump().get("streams") or {}) | set(
+        generated_transcode_streams(config)
+    )
+
+    if stream_name not in known:
+        return JSONResponse(
+            content={"success": False, "message": "Unknown stream"},
+            status_code=404,
+        )
+
+    kbps = await asyncio.to_thread(measure_stream_bitrate, stream_name)
+
+    if kbps is None:
+        return JSONResponse(
+            content={"success": False, "message": "Stream sent no data"},
+            status_code=502,
+        )
+
+    return JSONResponse(content={"success": True, "kbps": round(kbps)})
+
+
 @router.get("/ffprobe", dependencies=[Depends(require_role(["admin"]))])
 def ffprobe(request: Request, paths: str = "", detailed: bool = False):
     path_param = paths
@@ -1342,6 +1375,12 @@ async def delete_camera(
     except Exception:
         logger.debug("Failed to remove go2rtc stream for %s", camera_name)
 
+    await asyncio.to_thread(
+        sync_transcode_streams,
+        generated_transcode_streams(frigate_config),
+        generated_transcode_streams(request.app.frigate_config),
+    )
+
     return JSONResponse(
         content={
             "success": True,

frigate/api/config_util.py

@@ -51,6 +51,7 @@ def swap_runtime_config(app: FastAPI, config: FrigateConfig) -> None:
 
     if app.stats_emitter is not None:
         app.stats_emitter.config = config
+        app.stats_emitter.hardware_stats.set_config(config)
 
     if app.dispatcher is not None:
         app.dispatcher.config = config

frigate/api/defs/query/events_query_parameters.py

@@ -14,6 +14,7 @@ class EventsQueryParams(BaseModel):
     zone: str | None = "all"
     zones: str | None = "all"
     limit: int | None = 100
+    offset: int | None = Field(0, ge=0)
     after: float | None = None
     before: float | None = None
     time_range: str | None = DEFAULT_TIME_RANGE
@@ -55,6 +56,7 @@ class EventsSearchQueryParams(BaseModel):
         deprecated=True,
     )
     limit: int | None = 50
+    offset: int | None = Field(0, ge=0)
     cameras: str | None = "all"
     labels: str | None = "all"
     sub_labels: str | None = "all"

frigate/api/defs/request/app_body.py

@@ -10,6 +10,8 @@ class AppConfigSetBody(BaseModel):
     update_topic: str | None = None
     config_data: dict[str, Any] | None = None
     skip_save: bool = False
+    # paths rewritten whole, so a map saves in the order sent
+    replace_paths: list[str] = Field(default_factory=list)
 
 
 class GenAIProbeBody(BaseModel):

frigate/api/event.py

@@ -129,6 +129,7 @@ def events(
         zones = zone
 
     limit = params.limit
+    offset = params.offset
     after = params.after
     before = params.before
     time_range = params.time_range
@@ -361,11 +362,15 @@ def events(
     else:
         order_by = Event.start_time.desc()
 
+    # offset paging needs a stable order when scores or speeds tie
+    tiebreaker = [Event.id] if sort and sort.startswith(("score", "speed")) else []
+
     events = (
         Event.select(*selected_columns)
         .where(reduce(operator.and_, clauses))
-        .order_by(order_by)
+        .order_by(order_by, *tiebreaker)
         .limit(limit)
+        .offset(offset)
         .dicts()
         .iterator()
     )
@@ -534,6 +539,7 @@ def events_search(
     search_type = params.search_type
     include_thumbnails = params.include_thumbnails
     limit = params.limit
+    offset = params.offset
     sort = params.sort
 
     # Filters
@@ -840,6 +846,9 @@ def events_search(
     if search_results:
         events_query = events_query.where(Event.id << list(search_results.keys()))
 
+    # sorts below are stable, so this orders ties for offset paging
+    events_query = events_query.order_by(Event.id)
+
     # Fetch events and process them in a single pass
     processed_events = []
     for event in events_query.dicts():
@@ -897,7 +906,7 @@ def events_search(
         processed_events.sort(key=lambda x: x["start_time"], reverse=True)
 
     # Limit the number of events returned
-    processed_events = processed_events[:limit]
+    processed_events = processed_events[offset:][:limit]
 
     return JSONResponse(content=processed_events)
 

frigate/api/media.py

@@ -63,7 +63,6 @@ from frigate.util.recording_coverage import (
     null_audio_glitches,
     plan_clip,
     resolve_coverage,
-    stream_has_audio,
 )
 
 logger = logging.getLogger(__name__)
@@ -681,15 +680,10 @@ async def _vod_response(
         end_ts,
         force_discontinuity,
     )
-    intervals = resolve_coverage(camera_name, start_ts, end_ts)
-
     # rows contradicting their stream's audio composition are
     # truncated-shutdown glitches
-    main_audio = stream_has_audio(intervals, main=True)
-    sub_audio = stream_has_audio(intervals, main=False)
-
     spans = build_spans(
-        null_audio_glitches(intervals, main_audio, sub_audio),
+        null_audio_glitches(resolve_coverage(camera_name, start_ts, end_ts)),
         stream_preference,
     )
 

frigate/api/review.py

@@ -44,6 +44,22 @@ logger = logging.getLogger(__name__)
 router = APIRouter(tags=[Tags.review])
 
 
+def get_label_clause(label: str, include_audio: bool = True):
+    """Build a clause matching a label within a review segment's data.
+
+    Verified objects are stored with a `-verified` suffix (eg. `person-verified`)
+    so that variant is matched as well.
+    """
+    clause = (ReviewSegment.data["objects"].cast("text") % f'*"{label}"*') | (
+        ReviewSegment.data["objects"].cast("text") % f'*"{label}-verified"*'
+    )
+
+    if include_audio:
+        clause |= ReviewSegment.data["audio"].cast("text") % f'*"{label}"*'
+
+    return clause
+
+
 @router.get(
     "/review",
     response_model=list[ReviewSegmentResponse],
@@ -93,10 +109,7 @@ async def review(
         filtered_labels = labels.split(",")
 
         for label in filtered_labels:
-            label_clauses.append(
+            label_clauses.append(get_label_clause(label))
-                (ReviewSegment.data["objects"].cast("text") % f'*"{label}"*')
-                | (ReviewSegment.data["audio"].cast("text") % f'*"{label}"*')
-            )
         clauses.append(reduce(operator.or_, label_clauses))
 
     if zones != "all":
@@ -239,10 +252,7 @@ async def review_summary(
         filtered_labels = labels.split(",")
 
         for label in filtered_labels:
-            label_clauses.append(
+            label_clauses.append(get_label_clause(label))
-                (ReviewSegment.data["objects"].cast("text") % f'*"{label}"*')
-                | (ReviewSegment.data["audio"].cast("text") % f'*"{label}"*')
-            )
         clauses.append(reduce(operator.or_, label_clauses))
     if zones != "all":
         # use matching so segments with multiple zones
@@ -340,9 +350,8 @@ async def review_summary(
         filtered_labels = labels.split(",")
 
         for label in filtered_labels:
-            label_clauses.append(
+            label_clauses.append(get_label_clause(label, include_audio=False))
-                ReviewSegment.data["objects"].cast("text") % f'*"{label}"*'
+
-            )
         clauses.append(reduce(operator.or_, label_clauses))
 
     # Find the time range of available data

frigate/app.py

@@ -49,7 +49,6 @@ from frigate.debug_replay import (
     DebugReplayManager,
     cleanup_replay_cameras,
 )
-from frigate.detectors.detector_config import SceneEnum
 from frigate.detectors.detector_types import api_types
 from frigate.detectors.device import build_detector_config, runner_names
 from frigate.embeddings import EmbeddingProcess, EmbeddingsContext
@@ -78,7 +77,7 @@ from frigate.notices.registry import NoticeRegistry
 from frigate.object_detection.base import ObjectDetectProcess
 from frigate.object_detection.util import detection_frame_size
 from frigate.output.output import OutputProcess
-from frigate.ptz.autotrack import PtzAutoTrackerThread
+from frigate.ptz.autotrack import PtzAutoTracker
 from frigate.ptz.onvif import OnvifController
 from frigate.record.cleanup import RecordingCleanup
 from frigate.record.export import migrate_exports
@@ -108,7 +107,7 @@ class FrigateApp:
         self.metrics_manager = manager
         self.audio_process: mp.Process | None = None
         self.stop_event = stop_event
-        self.detection_queues: dict[SceneEnum, Queue] = {
+        self.detection_queues: dict[str, Queue] = {
             model.scene: mp.Queue() for model in config.models
         }
         self.detectors: dict[str, ObjectDetectProcess] = {}
@@ -167,11 +166,7 @@ class FrigateApp:
         # create camera_metrics
         for camera_name in self.config.cameras.keys():
             self.camera_metrics[camera_name] = CameraMetrics(self.metrics_manager)
-            self.ptz_metrics[camera_name] = PTZMetrics(
+            self.ptz_metrics[camera_name] = PTZMetrics()
-                autotracker_enabled=self.config.cameras[
-                    camera_name
-                ].onvif.autotracking.enabled
-            )
 
     def init_queues(self) -> None:
         # Queue for cameras to push tracked objects to
@@ -395,7 +390,7 @@ class FrigateApp:
                 logger.error("Unable to prepare the %s runtime: %s", detector_type, err)
 
     def start_detectors(self) -> None:
-        model_cameras: dict[SceneEnum, list[str]] = {
+        model_cameras: dict[str, list[str]] = {
             model.scene: [] for model in self.config.models
         }
 
@@ -444,7 +439,7 @@ class FrigateApp:
                 )
 
     def start_ptz_autotracker(self) -> None:
-        self.ptz_autotracker_thread = PtzAutoTrackerThread(
+        self.ptz_autotracker_thread = PtzAutoTracker(
             self.config,
             self.onvif_controller,
             self.ptz_metrics,

frigate/camera/__init__.py

@@ -43,8 +43,6 @@ class CameraMetrics:
 
 
 class PTZMetrics:
-    autotracker_enabled: Synchronized
-
     start_time: Synchronized
     stop_time: Synchronized
     frame_time: Synchronized
@@ -52,13 +50,10 @@ class PTZMetrics:
     max_zoom: Synchronized
     min_zoom: Synchronized
 
-    tracking_active: Event
     motor_stopped: Event
     reset: Event
 
-    def __init__(self, *, autotracker_enabled: bool):
+    def __init__(self) -> None:
-        self.autotracker_enabled = mp.Value("i", autotracker_enabled)  # type: ignore[assignment]
-
         self.start_time = mp.Value("d", 0)  # type: ignore[assignment]
         self.stop_time = mp.Value("d", 0)  # type: ignore[assignment]
         self.frame_time = mp.Value("d", 0)  # type: ignore[assignment]
@@ -66,7 +61,6 @@ class PTZMetrics:
         self.max_zoom = mp.Value("d", 0)  # type: ignore[assignment]
         self.min_zoom = mp.Value("d", 0)  # type: ignore[assignment]
 
-        self.tracking_active = mp.Event()
         self.motor_stopped = mp.Event()
         self.reset = mp.Event()
 

frigate/camera/activity_manager.py

@@ -104,12 +104,13 @@ class CameraActivityManager:
         all_objects: list[dict[str, Any]] = []
 
         for camera in new_activity.keys():
-            if camera not in self.config.cameras:
+            camera_config = self.config.cameras.get(camera)
+            if camera_config is None:
                 continue
 
             # handle cameras that were added dynamically
             if camera not in self.camera_all_object_counts:
-                self.__init_camera(self.config.cameras[camera])
+                self.__init_camera(camera_config)
 
             new_objects = new_activity[camera].get("objects", [])
             all_objects.extend(new_objects)
@@ -234,12 +235,13 @@ class AudioActivityManager:
         now = datetime.datetime.now().timestamp()
 
         for camera in new_activity.keys():
-            if camera not in self.config.cameras:
+            camera_config = self.config.cameras.get(camera)
+            if camera_config is None:
                 continue
 
             # handle cameras that were added dynamically
             if camera not in self.current_audio_detections:
-                self.__init_camera(self.config.cameras[camera])
+                self.__init_camera(camera_config)
 
             new_detections = new_activity[camera].get("detections", [])
             if self.compare_audio_activity(camera, new_detections, now):

frigate/camera/maintainer.py

@@ -15,7 +15,6 @@ from frigate.config.camera.updater import (
     CameraConfigUpdateSubscriber,
 )
 from frigate.const import REPLAY_CAMERA_PREFIX
-from frigate.detectors.detector_config import SceneEnum
 from frigate.models import Regions
 from frigate.object_detection.util import detection_frame_size
 from frigate.util.builtin import empty_and_close_queue
@@ -31,7 +30,7 @@ class CameraMaintainer(threading.Thread):
     def __init__(
         self,
         config: FrigateConfig,
-        detection_queues: dict[SceneEnum, Queue],
+        detection_queues: dict[str, Queue],
         detected_frames_queue: Queue,
         camera_metrics: DictProxy,
         ptz_metrics: dict[str, PTZMetrics],
@@ -121,9 +120,7 @@ class CameraMaintainer(threading.Thread):
 
         if runtime:
             self.camera_metrics[name] = CameraMetrics(self.metrics_manager)
-            self.ptz_metrics[name] = PTZMetrics(
+            self.ptz_metrics[name] = PTZMetrics()
-                autotracker_enabled=config.onvif.autotracking.enabled
-            )
             self.region_grids[name] = get_camera_regions_grid(
                 name,
                 config.detect,

frigate/camera/state.py

@@ -16,7 +16,7 @@ from frigate.config import (
     ZoomingModeEnum,
 )
 from frigate.const import CLIPS_DIR, THUMB_DIR
-from frigate.ptz.autotrack import PtzAutoTrackerThread
+from frigate.ptz.autotrack import PtzAutoTracker, calculate_max_target_box
 from frigate.track.tracked_object import TrackedObject
 from frigate.util.image import (
     SharedMemoryFrameManager,
@@ -35,7 +35,7 @@ class CameraState:
         name: str,
         config: FrigateConfig,
         frame_manager: SharedMemoryFrameManager,
-        ptz_autotracker_thread: PtzAutoTrackerThread,
+        ptz_autotracker_thread: PtzAutoTracker,
     ) -> None:
         self.name = name
         self.config = config
@@ -115,17 +115,13 @@ class CameraState:
                 # draw thicker box around ptz autotracked object
                 if (
                     self.camera_config.onvif.autotracking.enabled
-                    and self.ptz_autotracker_thread.ptz_autotracker.autotracker_init.get(
+                    and self.ptz_autotracker_thread.autotracker_init.get(self.name)
-                        self.name
+                    and self.ptz_autotracker_thread.tracked_object[self.name]
-                    )
-                    and self.ptz_autotracker_thread.ptz_autotracker.tracked_object[
-                        self.name
-                    ]
                     is not None
                     and obj["id"]
-                    == self.ptz_autotracker_thread.ptz_autotracker.tracked_object[
+                    == self.ptz_autotracker_thread.tracked_object[  # type: ignore[union-attr]
                         self.name
-                    ].obj_data["id"]  # type: ignore[attr-defined]
+                    ].obj_data["id"]
                     and obj["frame_time"] == frame_time
                 ):
                     thickness = 5
@@ -138,9 +134,9 @@ class CameraState:
                         and self.camera_config.detect.width is not None
                         and self.camera_config.detect.height is not None
                     ):
-                        max_target_box = self.ptz_autotracker_thread.ptz_autotracker.tracked_object_metrics[
+                        max_target_box = calculate_max_target_box(
-                            self.name
+                            self.camera_config.onvif.autotracking.zoom_factor
-                        ]["max_target_box"]  # type: ignore[index]
+                        )
                         side_length = max_target_box * (
                             max(
                                 self.camera_config.detect.width,

frigate/comms/detections_updater.py

@@ -12,6 +12,7 @@ class DetectionTypeEnum(str, Enum):
     video = "video"
     audio = "audio"
     lpr = "lpr"
+    classification_state = "classification_state"
 
 
 class DetectionPublisher(Publisher):

frigate/comms/dispatcher.py

@@ -759,15 +759,13 @@ class Dispatcher:
                     "Autotracking must be enabled in the config to be turned on via MQTT."
                 )
                 return
-            if not self.ptz_metrics[camera_name].autotracker_enabled.value:
+            if not ptz_autotracker_settings.enabled:
                 logger.info(f"Turning on ptz autotracker for {camera_name}")
-                self.ptz_metrics[camera_name].autotracker_enabled.value = True
                 self.ptz_metrics[camera_name].start_time.value = 0
                 ptz_autotracker_settings.enabled = True
         elif payload == "OFF":
-            if self.ptz_metrics[camera_name].autotracker_enabled.value:
+            if ptz_autotracker_settings.enabled:
                 logger.info(f"Turning off ptz autotracker for {camera_name}")
-                self.ptz_metrics[camera_name].autotracker_enabled.value = False
                 self.ptz_metrics[camera_name].start_time.value = 0
                 ptz_autotracker_settings.enabled = False
 
@@ -782,7 +780,9 @@ class Dispatcher:
         try:
             payload = int(payload)
         except ValueError:
+            logger.warning(
                 f"Received unsupported value for motion contour area: {payload}"
+            )
             return
 
         motion_settings = self.config.cameras[camera_name].motion
@@ -799,7 +799,9 @@ class Dispatcher:
         try:
             payload = int(payload)
         except ValueError:
+            logger.warning(
                 f"Received unsupported value for motion threshold: {payload}"
+            )
             return
 
         motion_settings = self.config.cameras[camera_name].motion
@@ -814,7 +816,9 @@ class Dispatcher:
     def _on_global_notification_command(self, payload: str) -> None:
         """Callback for global notification topic."""
         if payload != "ON" and payload != "OFF":
+            logger.warning(
                 f"Received unsupported value for all notification: {payload}"
+            )
             return
 
         notification_settings = self.config.notifications

frigate/comms/mqtt.py

@@ -2,6 +2,8 @@ from __future__ import annotations
 
 import logging
 import queue
+import selectors
+import socket
 import threading
 import time
 from collections.abc import Callable
@@ -56,6 +58,11 @@ class MqttClient(Communicator):
         self._next_connect_time = 0.0
         self._last_on_connect_dispatch = 0.0
 
+        # lets other threads interrupt the worker's socket wait
+        self._wake_recv, self._wake_send = socket.socketpair()
+        self._wake_recv.setblocking(False)
+        self._wake_send.setblocking(False)
+
     def subscribe(self, receiver: Callable) -> None:
         """Wrapper for allowing dispatcher to subscribe."""
         self._dispatcher = receiver
@@ -85,6 +92,7 @@ class MqttClient(Communicator):
             return
 
         self._publish_queue.put(QueuedPublish(full_topic, payload, retain))
+        self._wake_worker()
 
     def stop(self) -> None:
         if self._worker is None:
@@ -100,9 +108,11 @@ class MqttClient(Communicator):
                     publish_done,
                 )
             )
+            self._wake_worker()
             publish_done.wait(MQTT_SHUTDOWN_FLUSH_TIMEOUT)
 
         self._stop_event.set()
+        self._wake_worker()
 
         if self.client is not None:
             try:
@@ -358,7 +368,7 @@ class MqttClient(Communicator):
             deadline = time.monotonic() + MQTT_SHUTDOWN_FLUSH_TIMEOUT
             while not message_info.is_published() and time.monotonic() < deadline:
                 if (
-                    self.client.loop(timeout=MQTT_PUBLISH_WAIT_INTERVAL)
+                    self._loop_client(timeout=MQTT_PUBLISH_WAIT_INTERVAL)
                     != mqtt.MQTT_ERR_SUCCESS
                 ):
                     break
@@ -368,6 +378,51 @@ class MqttClient(Communicator):
                 exc_info=True,
             )
 
+    def _wake_worker(self) -> None:
+        try:
+            self._wake_send.send(b"\0")
+        except BlockingIOError:
+            # the buffer is full, so a wake is already pending
+            pass
+
+    def _loop_client(self, timeout: float) -> int:
+        """Drive Paho without select()'s limit on socket file descriptors."""
+        assert self.client is not None
+        client = self.client
+        sock = client.socket()
+        if sock is None:
+            return mqtt.MQTT_ERR_NO_CONN
+
+        events = selectors.EVENT_READ
+        if client.want_write():
+            events |= selectors.EVENT_WRITE
+        # TLS can have decrypted bytes buffered even when the socket is not ready.
+        pending = hasattr(sock, "pending") and sock.pending() > 0
+        with selectors.DefaultSelector() as selector:
+            selector.register(sock, events)
+            selector.register(self._wake_recv, selectors.EVENT_READ)
+            ready = {
+                key.fileobj: mask
+                for key, mask in selector.select(0.0 if pending else timeout)
+            }
+
+        if self._wake_recv in ready:
+            try:
+                self._wake_recv.recv(4096)
+            except BlockingIOError:
+                pass
+
+        ready_events = ready.get(sock, 0)
+        if pending or ready_events & selectors.EVENT_READ:
+            result = client.loop_read()
+            if result != mqtt.MQTT_ERR_SUCCESS or client.socket() is None:
+                return result
+        if ready_events & selectors.EVENT_WRITE:
+            result = client.loop_write()
+            if result != mqtt.MQTT_ERR_SUCCESS or client.socket() is None:
+                return result
+        return client.loop_misc()
+
     def _mqtt_loop_worker(self) -> None:
         # The worker owns all socket I/O so reconnect, subscribe, and publish
         # ordering stays serialized in one place.
@@ -384,7 +439,7 @@ class MqttClient(Communicator):
 
             assert self.client is not None
             try:
-                result = self.client.loop(timeout=MQTT_LOOP_TIMEOUT)
+                result = self._loop_client(timeout=MQTT_LOOP_TIMEOUT)
             except (OSError, mqtt.WebsocketConnectionError) as err:
                 logger.warning("MQTT loop error: %s", err)
                 self._schedule_reconnect()
@@ -617,7 +672,7 @@ class MqttClient(Communicator):
                 return
 
             try:
-                result = self.client.loop(timeout=MQTT_PUBLISH_WAIT_INTERVAL)
+                result = self._loop_client(timeout=MQTT_PUBLISH_WAIT_INTERVAL)
             except (OSError, mqtt.WebsocketConnectionError) as err:
                 logger.warning("MQTT publish wait failed: %s", err)
                 self._schedule_reconnect()

frigate/config/camera/detect.py

@@ -1,6 +1,6 @@
 from pydantic import Field, model_validator
 
-from frigate.detectors.detector_config import SceneEnum
+from frigate.detectors.detector_config import DEFAULT_SCENE, SCENE_PATTERN
 
 from ..base import FrigateBaseModel
 
@@ -62,10 +62,11 @@ class DetectConfig(FrigateBaseModel):
         title="Detect width",
         description="Width (pixels) of frames used for the detect stream; leave empty to use the native stream resolution.",
     )
-    scene: SceneEnum = Field(
+    scene: str = Field(
-        default=SceneEnum.all,
+        default=DEFAULT_SCENE,
+        pattern=SCENE_PATTERN,
         title="Detect scene",
-        description="The environment this camera looks at, used to pick which of the configured models runs on it. Cameras left on 'all' run the model configured with a scene of 'all'.",
+        description="The environment this camera looks at, used to pick which of the configured models runs on it. Cameras left on 'default' run the model configured with a scene of 'default'.",
     )
     fps: int = Field(
         default=5,

frigate/config/camera/live.py

@@ -1,8 +1,57 @@
-from pydantic import Field
+from pydantic import Field, field_validator
+
+from frigate.util.live_streams import DEFAULT_TRANSCODE_QUALITIES
 
 from ..base import FrigateBaseModel
 
-__all__ = ["CameraLiveConfig"]
+__all__ = ["CameraLiveConfig", "LiveTranscodeConfig", "LiveTranscodeQualityConfig"]
+
+
+class LiveTranscodeQualityConfig(FrigateBaseModel):
+    height: int = Field(
+        ge=144,
+        le=2160,
+        title="Height",
+        description="Output height in pixels; width follows the source aspect ratio.",
+    )
+    bitrate: int = Field(
+        ge=64,
+        title="Bitrate",
+        description="Target and maximum video bitrate in kbps.",
+    )
+
+
+class LiveTranscodeConfig(FrigateBaseModel):
+    enabled: bool = Field(
+        default=False,
+        title="Enable transcoded streams",
+        description="Add lower-quality live streams that go2rtc transcodes in real time while someone is watching.",
+    )
+    source: str | None = Field(
+        default=None,
+        title="Source stream",
+        description="go2rtc stream to transcode. Defaults to the first live stream.",
+    )
+    qualities: list[LiveTranscodeQualityConfig] = Field(
+        default_factory=lambda: [
+            LiveTranscodeQualityConfig(**quality)
+            for quality in DEFAULT_TRANSCODE_QUALITIES
+        ],
+        title="Qualities",
+        description="One transcoded stream is added per quality.",
+    )
+
+    @field_validator("qualities")
+    @classmethod
+    def validate_unique_heights(
+        cls, qualities: list[LiveTranscodeQualityConfig]
+    ) -> list[LiveTranscodeQualityConfig]:
+        heights = [quality.height for quality in qualities]
+
+        if len(heights) != len(set(heights)):
+            raise ValueError("Transcoded stream heights must be unique.")
+
+        return qualities
 
 
 class CameraLiveConfig(FrigateBaseModel):
@@ -11,6 +60,11 @@ class CameraLiveConfig(FrigateBaseModel):
         title="Live stream names",
         description="Mapping of configured stream names to restream/go2rtc names used for live playback.",
     )
+    transcode: LiveTranscodeConfig = Field(
+        default_factory=LiveTranscodeConfig,
+        title="Transcoded streams",
+        description="Lower-quality live streams transcoded on demand by go2rtc.",
+    )
     height: int = Field(
         default=720,
         title="Live height",

frigate/config/camera/notification.py

@@ -1,6 +1,7 @@
 from pydantic import Field
 
 from ..base import FrigateBaseModel
+from ..env import EnvString
 
 __all__ = ["NotificationConfig"]
 
@@ -11,7 +12,7 @@ class NotificationConfig(FrigateBaseModel):
         title="Enable notifications",
         description="Enable or disable notifications for all cameras; can be overridden per-camera.",
     )
-    email: str | None = Field(
+    email: EnvString | None = Field(
         default=None,
         title="Notification email",
         description="Email address used for push notifications or required by certain notification providers.",

frigate/config/config.py

@@ -19,7 +19,7 @@ from ruamel.yaml import YAML
 
 from frigate.const import REGEX_JSON
 from frigate.detectors import ModelConfig
-from frigate.detectors.detector_config import SceneEnum
+from frigate.detectors.detector_config import DEFAULT_SCENE
 from frigate.detectors.device import DeviceParseError, DeviceSpec, parse_device
 from frigate.plus import PlusApi
 from frigate.util.builtin import (
@@ -35,6 +35,13 @@ from frigate.util.config import (
     migrate_frigate_config,
 )
 from frigate.util.image import create_mask
+from frigate.util.live_streams import (
+    default_transcode_source,
+    is_transcode_stream_name,
+    transcode_stream_name,
+    transcode_streams,
+)
+from frigate.util.runtime_deps import sha256_of
 from frigate.util.services import auto_detect_hwaccel
 
 from .auth import AuthConfig
@@ -282,11 +289,78 @@ def verify_config_roles(camera_config: CameraConfig) -> None:
         )
 
 
+def apply_live_transcode_streams(
+    frigate_config: FrigateConfig, camera_config: CameraConfig
+) -> None:
+    """Fold a camera's transcoded streams into its live stream list.
+
+    Enabled qualities missing from live.streams are appended, and entries the
+    user placed keep their position. Transcoded names that are no longer
+    generated are dropped unless they name a real go2rtc stream.
+    """
+    live = camera_config.live
+    transcode = live.transcode
+    go2rtc_streams = frigate_config.go2rtc.model_dump().get("streams") or {}
+    generated: dict[str, str] = {}
+
+    if transcode.enabled:
+        if transcode.source is None:
+            transcode.source = default_transcode_source(
+                camera_config.name, live.streams
+            )
+
+        if transcode.source not in go2rtc_streams:
+            raise ValueError(
+                f"Camera {camera_config.name} has transcoded streams enabled, but its source {transcode.source} is not a go2rtc stream."
+            )
+
+        generated = transcode_streams(
+            camera_config.name,
+            transcode.source,
+            [quality.model_dump() for quality in transcode.qualities],
+        )
+
+        for name in generated:
+            if name in go2rtc_streams:
+                raise ValueError(
+                    f"Camera {camera_config.name} generates transcoded stream {name}, which collides with a go2rtc stream of the same name."
+                )
+
+    streams = {
+        label: name
+        for label, name in live.streams.items()
+        if name in generated
+        or name in go2rtc_streams
+        or not is_transcode_stream_name(camera_config.name, name)
+    }
+    placed = set(streams.values())
+
+    for quality in transcode.qualities if transcode.enabled else []:
+        name = transcode_stream_name(camera_config.name, quality.height)
+
+        if name in placed:
+            continue
+
+        label = f"{quality.height}p"
+
+        if label in streams:
+            raise ValueError(
+                f"Camera {camera_config.name} already has a live stream named {label}; rename it or place the transcoded stream under another name."
+            )
+
+        streams[label] = name
+
+    live.streams = streams
+
+
 def verify_valid_live_stream_names(
     frigate_config: FrigateConfig, camera_config: CameraConfig
 ) -> ValueError | None:
     """Verify that a restream exists to use for live view."""
     for _, stream_name in camera_config.live.streams.items():
+        if is_transcode_stream_name(camera_config.name, stream_name):
+            continue
+
         if (
             stream_name
             not in frigate_config.go2rtc.model_dump().get("streams", {}).keys()
@@ -536,7 +610,7 @@ class FrigateConfig(FrigateBaseModel):
     models: list[ModelConfig] = Field(
         default_factory=_default_models,
         title="Detection models",
-        description="Object detection models and the hardware each one runs on. Cameras pick a model by matching their detect.scene against a model's scene.",
+        description="Object detection models and the hardware each one runs on. Cameras pick a model by matching their detect.scene against a model's scene, falling back to the 'default' model.",
     )
 
     # GenAI config (named provider configs: name -> GenAIConfig)
@@ -651,7 +725,9 @@ class FrigateConfig(FrigateBaseModel):
     )
 
     _plus_api: PlusApi
-    _model_devices: dict[SceneEnum, list[DeviceSpec]]
+    _model_devices: dict[str, list[DeviceSpec]]
+    # scene -> model, including the scenes of duplicate models folded into another
+    _scene_models: dict[str, ModelConfig]
     _camera_models: dict[str, ModelConfig]
     _all_attributes: list[str]
     _all_attribute_logos: list[str]
@@ -686,7 +762,7 @@ class FrigateConfig(FrigateBaseModel):
     def primary_model(self) -> ModelConfig:
         """The model used when no specific camera is in play."""
         for model in self.models:
-            if model.scene == SceneEnum.all:
+            if model.scene == DEFAULT_SCENE:
                 return model
 
         return self.models[0]
@@ -708,7 +784,7 @@ class FrigateConfig(FrigateBaseModel):
 
         if model is None:
             camera = self.cameras.get(camera_name)
-            scene = camera.detect.scene if camera is not None else SceneEnum.all
+            scene = camera.detect.scene if camera is not None else DEFAULT_SCENE
             model = self._resolve_camera_model(camera_name, scene)
             self._camera_models[camera_name] = model
 
@@ -768,14 +844,14 @@ class FrigateConfig(FrigateBaseModel):
         if not self.models:
             raise ValueError("At least one model must be configured under models")
 
-        model_devices: dict[SceneEnum, list[DeviceSpec]] = {}
+        model_devices: dict[str, list[DeviceSpec]] = {}
         # device string -> the scene of the model that already claimed it
-        claimed_devices: dict[str, SceneEnum] = {}
+        claimed_devices: dict[str, str] = {}
 
         for index, model in enumerate(self.models):
-            scene = model.scene.value
+            scene = model.scene
 
-            if model.scene in model_devices:
+            if scene in model_devices:
                 raise ValueError(
                     f"Multiple models are configured with a scene of '{scene}'. Each model must use a different scene."
                 )
@@ -804,17 +880,20 @@ class FrigateConfig(FrigateBaseModel):
                     other = claimed_devices[device.raw]
                     where = (
                         f"twice by model '{scene}'"
-                        if other == model.scene
+                        if other == scene
-                        else f"by both the '{other.value}' and '{scene}' models"
+                        else f"by both the '{other}' and '{scene}' models"
                     )
                     raise ValueError(
                         f"Device '{device.raw}' is used {where}, but it can only run one detection process."
                     )
 
-                claimed_devices[device.raw] = model.scene
+                claimed_devices[device.raw] = scene
 
             self.models[index] = self._load_model(model, devices[0].detector)
-            model_devices[model.scene] = devices
+            model_devices[scene] = devices
+
+        self._scene_models = {model.scene: model for model in self.models}
+        self._consolidate_duplicate_models(model_devices)
 
         attributes: set[str] = set()
         attribute_logos: set[str] = set()
@@ -838,36 +917,107 @@ class FrigateConfig(FrigateBaseModel):
         }
         self._all_labels = labels
 
-    def _resolve_camera_model(self, name: str, scene: SceneEnum) -> ModelConfig:
+    def _consolidate_duplicate_models(
+        self, model_devices: dict[str, list[DeviceSpec]]
+    ) -> None:
+        """Fold models that load the same model file into a single model.
+
+        Separate scenes for one model only split the same work across separate
+        detection queues, so each device serves fewer cameras and is slower
+        overall than one shared model. The duplicate's devices are moved to the
+        model it duplicates and its scene resolves to that model.
+
+        Args:
+            model_devices: Scene to parsed devices, updated in place
+        """
+        kept: list[ModelConfig] = []
+        hashes: dict[str, str | None] = {}
+
+        def file_hash(path: str) -> str | None:
+            if path not in hashes:
+                hashes[path] = sha256_of(path) if os.path.isfile(path) else None
+
+            return hashes[path]
+
+        def same_model(a: ModelConfig, b: ModelConfig) -> bool:
+            # a model's devices all share a detector, so folding across
+            # detectors would produce an invalid model
+            if model_devices[a.scene][0].detector != model_devices[b.scene][0].detector:
+                return False
+
+            if not a.path or not b.path:
+                return False
+
+            if os.path.realpath(a.path) == os.path.realpath(b.path):
+                return True
+
+            a_hash = file_hash(a.path)
+            return a_hash is not None and a_hash == file_hash(b.path)
+
+        for model in self.models:
+            original = next((other for other in kept if same_model(other, model)), None)
+
+            if original is None:
+                kept.append(model)
+                continue
+
+            # keep the default model so cameras without a scene still find it
+            if model.scene == DEFAULT_SCENE:
+                kept[kept.index(original)] = model
+                original, model = model, original
+
+            logger.warning(
+                "Models '%s' and '%s' use the same model file, so they have been combined into the '%s' model. Defining one model under several scenes to assign detectors to specific cameras is slower and less efficient than letting every detector serve every camera. Remove the '%s' model and list its devices under the '%s' model instead",
+                original.scene,
+                model.scene,
+                original.scene,
+                model.scene,
+                original.scene,
+            )
+            original.devices = [*original.devices, *model.devices]
+            model_devices[original.scene] = [
+                *model_devices[original.scene],
+                *model_devices.pop(model.scene),
+            ]
+            self._scene_models[model.scene] = original
+
+            # anything already folded into the duplicate follows it
+            for scene, target in self._scene_models.items():
+                if target is model:
+                    self._scene_models[scene] = original
+
+        self.models = kept
+
+    def _resolve_camera_model(self, name: str, scene: str) -> ModelConfig:
         """Resolve which model a camera runs on.
 
         A camera may name a scene no model is configured for, which is valid as
-        long as an 'all' model is there to fall back to.
+        long as a 'default' model is there to fall back to.
 
         Args:
             name: Name of the camera
-            scene: The camera's detect scene, which defaults to 'all'
+            scene: The camera's detect scene, which defaults to 'default'
 
         Returns:
             The model the camera runs on
         """
-        by_scene = {model.scene: model for model in self.models}
+        model = self._scene_models.get(scene)
-        model = by_scene.get(scene)
 
         if model is not None:
             return model
 
-        default = by_scene.get(SceneEnum.all)
+        default = self._scene_models.get(DEFAULT_SCENE)
 
         if default is None:
             raise ValueError(
-                f"Camera '{name}' has a detect scene of '{scene.value}', but no model is configured for that scene or for 'all'."
+                f"Camera '{name}' has a detect scene of '{scene}', but no model is configured for that scene or for '{DEFAULT_SCENE}'."
             )
 
         logger.warning(
-            "Camera '%s' has a detect scene of '%s', but no model is configured for that scene, so the 'all' model is used",
+            "Camera '%s' has a detect scene of '%s', but no model is configured for that scene, so the '%s' model is used",
             name,
-            scene.value,
+            scene,
+            DEFAULT_SCENE,
         )
         return default
 
@@ -978,6 +1128,8 @@ class FrigateConfig(FrigateBaseModel):
                 "face_recognition": ["enabled", "min_area"],
                 "lpr": ["enabled", "expire_time", "min_area", "enhancement"],
                 "audio_transcription": ["enabled", "live_enabled"],
+                # transcode is camera-level only
+                "live": ["streams", "height", "quality"],
             }
 
             for section in allowed_fields_map:
@@ -998,6 +1150,10 @@ class FrigateConfig(FrigateBaseModel):
             camera_model = self._resolve_camera_model(name, camera_config.detect.scene)
             self._camera_models[name] = camera_model
 
+            # point cameras at the model their duplicate scene was folded into
+            if camera_config.detect.scene in self._scene_models:
+                camera_config.detect.scene = camera_model.scene
+
             if camera_config.ffmpeg.hwaccel_args == "auto":
                 camera_config.ffmpeg.hwaccel_args = self.ffmpeg.hwaccel_args
 
@@ -1206,6 +1362,8 @@ class FrigateConfig(FrigateBaseModel):
             if not camera_config.live.streams:
                 camera_config.live.streams = {name: name}
 
+            apply_live_transcode_streams(self, camera_config)
+
             # generate the ffmpeg commands
             camera_config.create_ffmpeg_cmds()
             self.cameras[name] = camera_config

frigate/data_processing/common/license_plate/mixin.py

@@ -176,6 +176,7 @@ class LicensePlateProcessingMixin:
         """
         input_shape = [3, 48, 320]
         num_images = len(images)
+        outputs: list[np.ndarray] = []
 
         for index in range(0, num_images, self.batch_size):
             input_h, input_w = input_shape[1], input_shape[2]
@@ -196,7 +197,7 @@ class LicensePlateProcessingMixin:
                 norm_images.append(norm_image)
 
             try:
-            outputs = self.model_runner.recognition_model(norm_images)  # type: ignore[arg-type]
+                outputs.extend(self.model_runner.recognition_model(norm_images))  # type: ignore[arg-type]
             except Exception as e:
                 logger.warning(f"Error running LPR recognition model: {e}")
                 return [], []

frigate/data_processing/post/review_annotations.py

@@ -1,10 +1,10 @@
 """Frame annotations derived from object tracking data.
 
 Builds short notes describing what changed during a review item, keyed to the
-frames sampled from it. Everything here comes from tracked object data already
+frames sampled from it. Everything here comes from data already recorded
-in the database (each event's `path_data` trajectory and the timeline's
+(each event's `path_data` trajectory, the timeline's stationary/active
-stationary/active changes), so the notes can be stated to the model as fact
+changes, and the review item's state classification changes), so the notes
-rather than as something it must perceive.
+can be stated to the model as fact rather than as something it must perceive.
 """
 
 import logging
@@ -95,6 +95,15 @@ def event_name(event: dict[str, Any]) -> str:
     return f"{article} {label}"
 
 
+def describe_classification_change(change: dict[str, Any]) -> str:
+    """Phrase a state classification change, e.g. 'front gate changed from
+    closed to open'."""
+    model = str(change["model"]).replace("_", " ")
+    before = str(change["from"]).replace("_", " ")
+    after = str(change["to"]).replace("_", " ")
+    return f"{model} changed from {before} to {after}"
+
+
 def path_legs(points: list[Point]) -> list[Leg]:
     """Split a trajectory into runs of travel in a consistent direction.
 
@@ -359,25 +368,38 @@ def get_state_changes(detection_ids: list[str]) -> list[dict[str, Any]]:
 def build_frame_captions(
     detection_ids: list[str],
     frame_times: list[float],
+    classification_changes: Sequence[dict[str, Any]] = (),
 ) -> list[str]:
     """A caption for each sampled frame, in frame order.
 
     Every frame gets its index and elapsed time so the model can tell them
-    apart; frames where something changed also carry the tracker notes for
+    apart; frames where something changed also carry the tracker and state
-    that moment. Returns an empty list when there is nothing to say, which
+    classification notes for that moment. Returns an empty list when there is
-    callers treat as a reason to fall back to sending plain frames.
+    nothing to say, which callers treat as a reason to fall back to sending
+    plain frames.
     """
     if not frame_times:
         return []
 
+    span_end = frame_times[-1]
+    timeline: list[tuple[float, str]] = []
     events = get_tracked_events(detection_ids)
 
-    if not events:
+    # audio and manual review items can have state changes but no tracked objects
-        logger.debug("No tracked events found for review item, skipping annotations")
+    if events:
-        return []
+        timeline.extend(
+            (timestamp, f"[tracker] {note}")
+            for timestamp, note in build_timeline(
+                events, span_end, get_state_changes(detection_ids)
+            )
+        )
 
-    timeline = build_timeline(events, frame_times[-1], get_state_changes(detection_ids))
+    timeline.extend(
-    buckets = annotations_by_frame(timeline, frame_times)
+        (change["timestamp"], f"[state] {describe_classification_change(change)}")
+        for change in classification_changes
+        if change["timestamp"] <= span_end
+    )
+    buckets = annotations_by_frame(sorted(timeline, key=lambda m: m[0]), frame_times)
 
     if not buckets:
         return []
@@ -388,7 +410,7 @@ def build_frame_captions(
 
     for index, timestamp in enumerate(frame_times):
         lines = [f"Frame {index + 1} of {total} (+{timestamp - origin:.1f}s):"]
-        lines.extend(f"[tracker] {note}" for note in buckets.get(index, []))
+        lines.extend(buckets.get(index, []))
         captions.append("\n".join(lines))
 
     return captions

frigate/data_processing/post/review_descriptions.py

@@ -40,7 +40,7 @@ from frigate.util.image import get_image_from_recording
 
 from ..post.api import PostProcessorApi
 from ..types import DataProcessorMetrics
-from .review_annotations import build_frame_captions
+from .review_annotations import build_frame_captions, describe_classification_change
 
 logger = logging.getLogger(__name__)
 
@@ -254,6 +254,10 @@ class ReviewDescriptionProcessor(PostProcessorApi):
                     "start_time": r["start_time"],
                     "end_time": r["end_time"],
                     "metadata": r["data"]["metadata"],
+                    "state_changes": [
+                        describe_classification_change(change)
+                        for change in sorted_classification_state_changes(r["data"])
+                    ],
                 }
                 for r in (
                     ReviewSegment.select(
@@ -298,6 +302,9 @@ class ReviewDescriptionProcessor(PostProcessorApi):
                 primary_item["start_time"] = primary_seg["start_time"]
                 primary_item["end_time"] = primary_seg["end_time"]
 
+                if primary_seg["state_changes"]:
+                    primary_item["state_changes"] = primary_seg["state_changes"]
+
                 # Find overlapping contextual items from other cameras
                 primary_start = primary_seg["start_time"]
                 primary_end = primary_seg["end_time"]
@@ -318,12 +325,23 @@ class ReviewDescriptionProcessor(PostProcessorApi):
                     seg_end = seg["end_time"]
 
                     if seg_start < primary_end and primary_start < seg_end:
-                        # Avoid duplicates if same camera has multiple overlapping segments
+                        # Avoid duplicates if same camera has multiple overlapping
-                        if seg_camera not in seen_contextual_cameras:
+                        # segments. One with state changes is kept as its own item
+                        # so each change stays within its item's time range.
+                        if (
+                            seg_camera in seen_contextual_cameras
+                            and not seg["state_changes"]
+                        ):
+                            continue
+
                         contextual_item = copy.deepcopy(seg["metadata"])
                         contextual_item["camera"] = seg_camera
                         contextual_item["start_time"] = seg_start
                         contextual_item["end_time"] = seg_end
+
+                        if seg["state_changes"]:
+                            contextual_item["state_changes"] = seg["state_changes"]
+
                         contextual_items.append(contextual_item)
                         seen_contextual_cameras.add(seg_camera)
 
@@ -439,6 +457,7 @@ class ReviewDescriptionProcessor(PostProcessorApi):
             captions = build_frame_captions(
                 final_data["data"].get("detections") or [],
                 [timestamp for _, timestamp in frames],
+                sorted_classification_state_changes(final_data["data"]),
             )
 
             if not captions:
@@ -688,6 +707,39 @@ def get_recording_buffer_extension(duration: float) -> float:
     return buffer_extension
 
 
+def sorted_classification_state_changes(
+    review_data: dict[str, Any],
+) -> list[dict[str, Any]]:
+    """A review item's state classification changes in time order."""
+    return sorted(
+        review_data.get("classification_state_changes") or [],
+        key=lambda change: change["timestamp"],
+    )
+
+
+def format_classification_state_changes(
+    changes: list[dict[str, Any]], start_time: float, end_time: float
+) -> list[str]:
+    """Phrase state classification changes with their timing in the activity.
+
+    Changes are attached while the review item is active, which runs past its
+    end_time by the review cutoff, and a few seconds before its start.
+    """
+    lines = []
+
+    for change in changes:
+        if change["timestamp"] < start_time:
+            when = "just before the activity started"
+        elif change["timestamp"] > end_time:
+            when = "after the activity ended"
+        else:
+            when = f"{round(change['timestamp'] - start_time)}s into the activity"
+
+        lines.append(f"{describe_classification_change(change)}, {when}")
+
+    return lines
+
+
 def run_analysis(
     requestor: InterProcessRequestor,
     genai_client: GenAIClient,
@@ -743,6 +795,13 @@ def run_analysis(
                 unified_objects.append(object_type)
 
     analytics_data["unified_objects"] = unified_objects
+    analytics_data["classification_state_changes"] = (
+        format_classification_state_changes(
+            sorted_classification_state_changes(final_data["data"]),
+            final_data["start_time"],
+            final_data["end_time"],
+        )
+    )
 
     metadata = genai_client.generate_review_description(
         analytics_data,

frigate/data_processing/real_time/custom_classification.py

@@ -91,8 +91,23 @@ class CustomStateClassificationProcessor(DeferredRealtimeProcessorApi):
         self.tensor_input_details = self.interpreter.get_input_details()
         self.tensor_output_details = self.interpreter.get_output_details()
         self.labelmap = load_labels(labelmap_path, prefill=0, indexed=False)
+        self._forget_unknown_states()
         self.classifications_per_second.start()
 
+    def _forget_unknown_states(self) -> None:
+        """Drop verified states that are not labels of the loaded model.
+
+        A retrained model can rename or remove labels. Keeping a state it can
+        no longer produce would report its first verified state as a change
+        from that obsolete label.
+        """
+        labels = set(self.labelmap.values())
+        self.state_history = {
+            camera: history
+            for camera, history in self.state_history.items()
+            if history["current_state"] in labels
+        }
+
     def __update_metrics(self, duration: float) -> None:
         self.classifications_per_second.update()
         if self.inference_speed:
@@ -134,15 +149,20 @@ class CustomStateClassificationProcessor(DeferredRealtimeProcessorApi):
         # Don't save if state is stable (detected_state == current_state) AND score is 100%
         return False
 
-    def verify_state_change(self, camera: str, detected_state: str) -> str | None:
+    def verify_state_change(
+        self, camera: str, detected_state: str, timestamp: float
+    ) -> tuple[str | None, float] | None:
         """
         Verify state change requires 3 consecutive identical states before publishing.
-        Returns state to publish or None if verification not complete.
+        Returns (previous state, time the new state was first seen) once verified,
+        or None if verification not complete. The previous state is None for the
+        first state verified on a camera.
         """
         if camera not in self.state_history:
             self.state_history[camera] = {
                 "current_state": None,
                 "pending_state": None,
+                "pending_since": 0.0,
                 "consecutive_count": 0,
             }
 
@@ -157,12 +177,14 @@ class CustomStateClassificationProcessor(DeferredRealtimeProcessorApi):
             verification["consecutive_count"] += 1
 
             if verification["consecutive_count"] >= 3:
+                previous_state = verification["current_state"]
                 verification["current_state"] = detected_state
                 verification["pending_state"] = None
                 verification["consecutive_count"] = 0
-                return detected_state
+                return previous_state, verification["pending_since"]
         else:
             verification["pending_state"] = detected_state
+            verification["pending_since"] = timestamp
             verification["consecutive_count"] = 1
             logger.debug(
                 f"New state '{detected_state}' detected for {camera}, need {3 - verification['consecutive_count']} more consecutive detections"
@@ -340,16 +362,19 @@ class CustomStateClassificationProcessor(DeferredRealtimeProcessorApi):
             )
             return
 
-        verified_state = self.verify_state_change(camera, detected_state)
+        verified = self.verify_state_change(camera, detected_state, timestamp)
 
-        if verified_state is not None:
+        if verified is not None:
+            previous_state, changed_at = verified
             self._emit_result(
                 {
                     "type": "classification",
                     "processor": "state",
                     "model_name": self.model_config.name,
                     "camera": camera,
-                    "state": verified_state,
+                    "state": detected_state,
+                    "previous_state": previous_state,
+                    "timestamp": changed_at,
                 }
             )
 

frigate/db/sqlitevecq.py

@@ -1,8 +1,10 @@
 import logging
 import sqlite3
+import threading
 from typing import Any
 
 import regex
+from peewee import DatabaseError
 from playhouse.sqliteq import SqliteQueueDatabase
 
 logger = logging.getLogger(__name__)
@@ -17,6 +19,7 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase):
         self.load_vec_extension: bool = load_vec_extension
         # no extension necessary, sqlite will load correctly for each platform
         self.sqlite_vec_path = "/usr/local/lib/vec0"
+        self.upsert_lock = threading.Lock()
         super().__init__(*args, **kwargs)
 
     def _connect(self, *args: Any, **kwargs: Any) -> sqlite3.Connection:
@@ -53,6 +56,22 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase):
 
         conn.create_function("REGEXP", 2, regexp)
 
+    def execute_write(self, sql: str, params: Any = None) -> None:
+        """Run a write and wait for it, so that failures are raised here.
+
+        SqliteQueueDatabase hands non-SELECT statements to a writer thread and
+        stores any exception on the cursor it returns, so callers that ignore
+        that cursor never learn the write failed.
+        """
+        self.execute_sql(sql, params).fetchall()
+
+    def _table_exists(self, table: str) -> bool:
+        cursor = self.execute_sql(
+            "SELECT name FROM sqlite_master WHERE type = 'table' AND name = ?",
+            (table,),
+        )
+        return cursor.fetchone() is not None
+
     def _delete_embeddings(self, table: str, event_ids: list[str]) -> None:
         """Delete embeddings for the given events, if the table exists.
 
@@ -63,17 +82,17 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase):
             return
 
         # the embeddings tables are only created once semantic search has run
-        cursor = self.execute_sql(
+        if not self._table_exists(table):
-            "SELECT name FROM sqlite_master WHERE type = 'table' AND name = ?",
-            (table,),
-        )
-
-        if cursor.fetchone() is None:
             logger.debug("Skipping %s cleanup, table does not exist", table)
             return
 
         ids = ",".join(["?" for _ in event_ids])
-        self.execute_sql(f"DELETE FROM {table} WHERE id IN ({ids})", event_ids)
+
+        # callers treat cleanup as best effort, so log rather than propagate
+        try:
+            self.execute_write(f"DELETE FROM {table} WHERE id IN ({ids})", event_ids)
+        except DatabaseError:
+            logger.exception("Failed to delete embeddings from %s", table)
 
     def delete_embeddings_thumbnail(self, event_ids: list[str]) -> None:
         self._delete_embeddings("vec_thumbnails", event_ids)
@@ -81,25 +100,67 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase):
     def delete_embeddings_description(self, event_ids: list[str]) -> None:
         self._delete_embeddings("vec_descriptions", event_ids)
 
+    def _restore_vec_info_table(self, table: str) -> None:
+        """Recreate the _info shadow table a legacy vec0 table is missing.
+
+        sqlite-vec added _info in 0.1.6 and drops it unconditionally when a
+        table is destroyed, so tables written by Frigate 0.17 and earlier fail
+        to drop. An empty stub is enough, and leaving it unseeded keeps the
+        table reading as pre-0.1.10 if the drop does not follow.
+        """
+        if not self._table_exists(table) or self._table_exists(f"{table}_info"):
+            return
+
+        logger.debug("Restoring the %s_info shadow table before dropping", table)
+        self.execute_write(
+            f'CREATE TABLE "{table}_info" (key TEXT PRIMARY KEY, value ANY)'
+        )
+
     def drop_embeddings_tables(self) -> None:
-        self.execute_sql("""
+        for table in ("vec_descriptions", "vec_thumbnails"):
-            DROP TABLE vec_descriptions;
+            self._restore_vec_info_table(table)
-        """)
+            self.execute_write(f"DROP TABLE IF EXISTS {table}")
-        self.execute_sql("""
-            DROP TABLE vec_thumbnails;
-        """)
 
     def create_embeddings_tables(self) -> None:
         """Create vec0 virtual table for embeddings"""
-        self.execute_sql("""
+        self.execute_write("""
             CREATE VIRTUAL TABLE IF NOT EXISTS vec_thumbnails USING vec0(
                 id TEXT PRIMARY KEY,
                 thumbnail_embedding FLOAT[768] distance_metric=cosine
             );
         """)
-        self.execute_sql("""
+        self.execute_write("""
             CREATE VIRTUAL TABLE IF NOT EXISTS vec_descriptions USING vec0(
                 id TEXT PRIMARY KEY,
                 description_embedding FLOAT[768] distance_metric=cosine
             );
         """)
+
+    def upsert_embeddings(
+        self, table: str, column: str, embeddings: dict[str, bytes]
+    ) -> None:
+        """Write embeddings for the given event ids, replacing any that exist.
+
+        vec0 implements neither REPLACE nor UPSERT, so rows that are already
+        there have to be deleted first.
+        """
+        if not embeddings:
+            return
+
+        event_ids = list(embeddings.keys())
+        ids = ",".join(["?" for _ in event_ids])
+        params: list[Any] = []
+
+        for event_id in event_ids:
+            params.extend((event_id, embeddings[event_id]))
+
+        values = ", ".join(["(?, ?)"] * len(event_ids))
+
+        # reindexing and live embedding run on separate threads, and each write
+        # is queued separately, so the delete and the insert have to be held
+        # together or an interleaved pair fails on the vec0 primary key
+        with self.upsert_lock:
+            self.execute_write(f"DELETE FROM {table} WHERE id IN ({ids})", event_ids)
+            self.execute_write(
+                f"INSERT INTO {table}(id, {column}) VALUES {values}", params
+            )

frigate/detectors/detector_config.py

@@ -47,21 +47,17 @@ class ModelTypeEnum(str, Enum):
     yologeneric = "yolo-generic"
 
 
-class SceneEnum(str, Enum):
+# the scene of the model used by cameras that don't name one
-    """The camera environment a detection model is intended for."""
+DEFAULT_SCENE = "default"
-
+SCENE_PATTERN = r"^[A-Za-z0-9_-]+$"
-    all = "all"
-    indoor = "indoor"
-    outdoor = "outdoor"
-    indoor_thermal = "indoor_thermal"
-    outdoor_thermal = "outdoor_thermal"
 
 
 class ModelConfig(BaseModel):
-    scene: SceneEnum = Field(
+    scene: str = Field(
-        default=SceneEnum.all,
+        default=DEFAULT_SCENE,
+        pattern=SCENE_PATTERN,
         title="Model scene",
-        description="The camera environment this model is used for. Cameras select a model by setting detect.scene to a matching value, and 'all' is used by any camera that does not set one.",
+        description="A name for the camera environment this model is used for, such as 'thermal'. Cameras select a model by setting detect.scene to a matching value, and the 'default' model is used by any camera that does not set one.",
     )
     devices: list[str] = Field(
         default_factory=list,
@@ -123,6 +119,7 @@ class ModelConfig(BaseModel):
     _all_attributes: list[str] = PrivateAttr()
     _all_attribute_logos: list[str] = PrivateAttr()
     _model_hash: str = PrivateAttr()
+    _plus_id: str | None = PrivateAttr(default=None)
 
     @property
     def merged_labelmap(self) -> dict[int, str]:
@@ -148,6 +145,11 @@ class ModelConfig(BaseModel):
     def model_hash(self) -> str:
         return self._model_hash
 
+    @property
+    def plus_id(self) -> str | None:
+        """The Frigate+ model id, once a plus:// path has been resolved."""
+        return self._plus_id
+
     def __init__(self, **config):
         super().__init__(**config)
 
@@ -178,20 +180,34 @@ class ModelConfig(BaseModel):
         os.makedirs(MODEL_CACHE_DIR, exist_ok=True)
 
         model_id = self.path[7:]
+        self._plus_id = model_id
         self.path = os.path.join(MODEL_CACHE_DIR, model_id)
         model_info_path = f"{self.path}.json"
 
         # download the model if it doesn't exist
         if not os.path.isfile(self.path):
+            try:
                 download_url = plus_api.get_model_download_url(model_id)
                 r = requests.get(download_url)
+            except requests.exceptions.ConnectionError as e:
+                raise ValueError(
+                    f"Unable to connect to Frigate+ to download model {model_id}"
+                ) from e
+
             with open(self.path, "wb") as f:
                 f.write(r.content)
 
         # download the model info if it doesn't exist
         if not os.path.isfile(model_info_path):
+            try:
+                model_info = plus_api.get_model_info(model_id)
+            except requests.exceptions.ConnectionError as e:
+                raise ValueError(
+                    f"Unable to connect to Frigate+ to download model info for {model_id}"
+                ) from e
+
             with open(model_info_path, "w") as f:
-                json.dump(plus_api.get_model_info(model_id), f)
+                json.dump(model_info, f)
 
         model_info = load_plus_model_info(model_id)
 

frigate/embeddings/embeddings.py

@@ -6,9 +6,10 @@ import logging
 import os
 import threading
 import time
+from typing import Any
 
 import numpy as np
-from peewee import DoesNotExist, IntegrityError
+from peewee import DatabaseError, DoesNotExist, IntegrityError
 from PIL import Image
 from playhouse.shortcuts import model_to_dict
 
@@ -207,12 +208,10 @@ class Embeddings:
         embedding = self.vision_embedding([thumbnail])[0]
 
         if upsert:
-            self.db.execute_sql(
+            self.db.upsert_embeddings(
-                """
+                "vec_thumbnails",
-                INSERT OR REPLACE INTO vec_thumbnails(id, thumbnail_embedding)
+                "thumbnail_embedding",
-                VALUES(?, ?)
+                {event_id: serialize(embedding)},
-                """,
-                (event_id, serialize(embedding)),
             )
 
         self.image_inference_speed.update(datetime.datetime.now().timestamp() - start)
@@ -251,19 +250,12 @@ class Embeddings:
         embeddings = self.vision_embedding(valid_thumbs)
 
         if upsert:
-            items = []
+            items = {}
             for i in range(len(valid_ids)):
-                items.append(valid_ids[i])
+                items[valid_ids[i]] = serialize(embeddings[i])
-                items.append(serialize(embeddings[i]))
                 self.image_eps.update()
 
-            self.db.execute_sql(
+            self.db.upsert_embeddings("vec_thumbnails", "thumbnail_embedding", items)
-                """
-                INSERT OR REPLACE INTO vec_thumbnails(id, thumbnail_embedding)
-                VALUES {}
-                """.format(", ".join(["(?, ?)"] * len(valid_ids))),
-                items,
-            )
 
         duration = datetime.datetime.now().timestamp() - start
         self.image_inference_speed.update(duration / len(valid_ids))
@@ -277,12 +269,10 @@ class Embeddings:
         embedding = self.text_embedding([description])[0]
 
         if upsert:
-            self.db.execute_sql(
+            self.db.upsert_embeddings(
-                """
+                "vec_descriptions",
-                INSERT OR REPLACE INTO vec_descriptions(id, description_embedding)
+                "description_embedding",
-                VALUES(?, ?)
+                {event_id: serialize(embedding)},
-                """,
-                (event_id, serialize(embedding)),
             )
 
         self.text_inference_speed.update(datetime.datetime.now().timestamp() - start)
@@ -302,19 +292,14 @@ class Embeddings:
 
         if upsert:
             ids = list(event_descriptions.keys())
-            items = []
+            items = {}
 
             for i in range(len(ids)):
-                items.append(ids[i])
+                items[ids[i]] = serialize(embeddings[i])
-                items.append(serialize(embeddings[i]))
                 self.text_eps.update()
 
-            self.db.execute_sql(
+            self.db.upsert_embeddings(
-                """
+                "vec_descriptions", "description_embedding", items
-                INSERT OR REPLACE INTO vec_descriptions(id, description_embedding)
-                VALUES {}
-                """.format(", ".join(["(?, ?)"] * len(ids))),
-                items,
             )
 
         self.text_inference_speed.update(datetime.datetime.now().timestamp() - start)
@@ -322,6 +307,17 @@ class Embeddings:
         return embeddings
 
     def reindex(self) -> None:
+        """Rebuild every tracked object embedding from scratch."""
+        totals: dict[str, Any] = {"status": "indexing"}
+
+        try:
+            self._reindex(totals)
+        except DatabaseError:
+            logger.exception("Unable to reindex tracked object embeddings")
+            totals["status"] = "failed"
+            self.requestor.send_data(UPDATE_EMBEDDINGS_REINDEX_PROGRESS, totals)
+
+    def _reindex(self, totals: dict[str, Any]) -> None:
         logger.info("Indexing tracked object embeddings...")
 
         self.db.drop_embeddings_tables()
@@ -346,17 +342,24 @@ class Embeddings:
             batch_size = 32
         current_page = 1
 
-        totals = {
+        totals.update(
+            {
                 "thumbnails": 0,
                 "descriptions": 0,
-            "processed_objects": total_events - 1 if total_events < batch_size else 0,
+                "processed_objects": total_events - 1
+                if total_events < batch_size
+                else 0,
                 "total_objects": total_events,
                 "time_remaining": 0 if total_events < batch_size else -1,
                 "status": "indexing",
             }
+        )
 
         self.requestor.send_data(UPDATE_EMBEDDINGS_REINDEX_PROGRESS, totals)
 
+        # a single batch sends no progress, so the first message shows it nearly done
+        totals["processed_objects"] = 0
+
         events = (
             Event.select()
             .order_by(Event.start_time.desc())

frigate/embeddings/maintainer.py

@@ -11,7 +11,11 @@ from typing import Any
 from peewee import DoesNotExist
 
 from frigate.comms.config_updater import ConfigSubscriber
-from frigate.comms.detections_updater import DetectionSubscriber, DetectionTypeEnum
+from frigate.comms.detections_updater import (
+    DetectionPublisher,
+    DetectionSubscriber,
+    DetectionTypeEnum,
+)
 from frigate.comms.embeddings_updater import (
     EmbeddingsRequestEnum,
     EmbeddingsResponder,
@@ -168,6 +172,7 @@ class EmbeddingMaintainer(threading.Thread):
         )
         self.review_subscriber = ReviewDataSubscriber("")
         self.detection_subscriber = DetectionSubscriber(DetectionTypeEnum.video.value)
+        self.detection_publisher = DetectionPublisher(DetectionTypeEnum.all.value)
         self.embeddings_responder = EmbeddingsResponder()
         self.frame_manager = SharedMemoryFrameManager()
 
@@ -356,6 +361,7 @@ class EmbeddingMaintainer(threading.Thread):
         self.event_end_subscriber.stop()
         self.recordings_subscriber.stop()
         self.detection_subscriber.stop()
+        self.detection_publisher.stop()
         self.event_metadata_publisher.stop()
         self.event_metadata_subscriber.stop()
         self.embeddings_responder.stop()
@@ -851,6 +857,21 @@ class EmbeddingMaintainer(threading.Thread):
                         f"{result['camera']}/classification/{result['model_name']}",
                         result["state"],
                     )
+
+                    # the first state verified after startup is not a change
+                    if result["previous_state"] is not None:
+                        self.detection_publisher.publish(
+                            (
+                                result["camera"],
+                                {
+                                    "model": result["model_name"],
+                                    "from": result["previous_state"],
+                                    "to": result["state"],
+                                    "timestamp": result["timestamp"],
+                                },
+                            ),
+                            DetectionTypeEnum.classification_state.value,
+                        )
                 elif result["processor"] == "object":
                     object_id = result["object_id"]
                     camera = result["camera"]

frigate/events/cleanup.py

@@ -365,6 +365,7 @@ class EventCleanup(threading.Thread):
                     chunk = ids_to_delete[i : i + CHUNK_SIZE]
                     logger.debug(f"Deleting {len(chunk)} events from the database")
                     Event.delete().where(Event.id << chunk).execute()
+                    Timeline.delete().where(Timeline.source_id << chunk).execute()
 
                     # embeddings are always cleaned up, even when semantic search
                     # is disabled, so that they don't outlive their events

frigate/ffmpeg_presets.py

@@ -126,8 +126,8 @@ PRESETS_HW_ACCEL_SCALE = {
     "preset-apple-silicon-h264": "-r {0} -vf fps={0},scale={1}:{2}",
     "preset-apple-silicon-h265": "-r {0} -vf fps={0},scale={1}:{2}",
     FFMPEG_HWACCEL_VAAPI: "-r {0} -vf fps={0},scale_vaapi=w={1}:h={2},hwdownload,format=nv12",
-    "preset-intel-qsv-h264": "-r {0} -vf vpp_qsv=w={1}:h={2}:format=nv12,hwdownload,format=nv12,fps={0},format=yuv420p",
+    "preset-intel-qsv-h264": "-r {0} -vf fps={0},vpp_qsv=w={1}:h={2}:format=nv12,hwdownload,format=nv12,format=yuv420p",
-    "preset-intel-qsv-h265": "-r {0} -vf vpp_qsv=w={1}:h={2}:format=nv12,hwdownload,format=nv12,fps={0},format=yuv420p",
+    "preset-intel-qsv-h265": "-r {0} -vf fps={0},vpp_qsv=w={1}:h={2}:format=nv12,hwdownload,format=nv12,format=yuv420p",
     FFMPEG_HWACCEL_NVIDIA: "-r {0} -vf fps={0},scale_cuda=w={1}:h={2},hwdownload,format=nv12",
     "preset-jetson-h264": "-r {0}",  # scaled in decoder
     "preset-jetson-h265": "-r {0}",  # scaled in decoder

frigate/genai/plugins/llama_cpp.py

@@ -103,11 +103,10 @@ class LlamaCppClient(GenAIClient):
     _supports_reasoning: bool
     _image_token_cache: dict[tuple[int, int], int]
     _text_baseline_tokens: int | None
-    _media_marker: str
 
     @property
     def supports_embeddings(self) -> bool:
-        """llama.cpp exposes an /embeddings endpoint for any loaded model."""
+        """llama.cpp exposes a /v1/embeddings endpoint for any loaded model."""
         return True
 
     def _auth_headers(self) -> dict | None:
@@ -159,7 +158,6 @@ class LlamaCppClient(GenAIClient):
         self._supports_reasoning = False
         self._image_token_cache = {}
         self._text_baseline_tokens = None
-        self._media_marker = "<__media__>"
 
         base_url = (
             self.genai_config.base_url.rstrip("/")
@@ -187,7 +185,6 @@ class LlamaCppClient(GenAIClient):
         self._supports_audio = info["supports_audio"]
         self._supports_tools = info["supports_tools"]
         self._supports_reasoning = info["supports_reasoning"]
-        self._media_marker = info["media_marker"]
 
         logger.info(
             "llama.cpp model '%s' initialized — context: %s, vision: %s, audio: %s, tools: %s, reasoning: %s",
@@ -215,9 +212,7 @@ class LlamaCppClient(GenAIClient):
         `architecture.input_modalities` (text/image/audio) — the primary
         source. When proxied through llama-swap, the same entry carries
         `status.args` (server launch argv) and, for the loaded model,
-        `meta.n_ctx`. /props remains the only source for `media_marker`,
+        `meta.n_ctx`.
-        which the server randomizes per startup unless LLAMA_MEDIA_MARKER
-        is set.
         """
         info: dict[str, Any] = {
             "context_size": None,
@@ -225,7 +220,6 @@ class LlamaCppClient(GenAIClient):
             "supports_audio": False,
             "supports_tools": False,
             "supports_reasoning": False,
-            "media_marker": "<__media__>",
         }
 
         model_entry: dict[str, Any] | None = None
@@ -314,16 +308,8 @@ class LlamaCppClient(GenAIClient):
             # in the Jinja chat template itself.
             chat_template = props.get("chat_template") or ""
             info["supports_reasoning"] = "enable_thinking" in chat_template
-
-            media_marker = props.get("media_marker")
-            if isinstance(media_marker, str) and media_marker:
-                info["media_marker"] = media_marker
         except Exception as e:
-            logger.warning(
+            logger.warning("Failed to query llama.cpp /props endpoint: %s", e)
-                "Failed to query llama.cpp /props endpoint: %s. "
-                "Image embeddings may fail if the server randomized its media marker.",
-                e,
-            )
 
         return info
 
@@ -474,9 +460,6 @@ class LlamaCppClient(GenAIClient):
     def _transcribe_via_chat(self, audio: bytes, language: str | None) -> str | None:
         """Transcribe through /v1/chat/completions, for servers without the
         transcriptions route.
-
-        The _media_marker / multimodal_data convention is an /embeddings-only
-        protocol, so no marker-refresh retry is needed here.
         """
         prompt = "Transcribe the speech in this audio verbatim. Respond with the transcript only, and with nothing at all if there is no speech."
 
@@ -794,41 +777,16 @@ class LlamaCppClient(GenAIClient):
             )
         return result if result else None
 
-    def _refresh_media_marker(self) -> bool:
-        """Re-fetch /props and update the cached media marker if it changed.
-
-        The server randomizes the marker per startup (unless LLAMA_MEDIA_MARKER
-        is set), so a stale marker indicates a restart. Returns True iff the
-        marker was updated to a new value — used to gate a one-shot retry of
-        a failed embeddings request.
-        """
-        if self.provider is None:
-            return False
-        try:
-            props = self._fetch_llama_props(self.provider, self.genai_config.model)
-        except Exception as e:
-            logger.warning("Failed to refresh llama.cpp media marker: %s", e)
-            return False
-
-        marker = props.get("media_marker")
-
-        if not isinstance(marker, str) or not marker or marker == self._media_marker:
-            return False
-
-        logger.info("llama.cpp media marker changed (server restart); refreshed")
-        self._media_marker = marker
-        return True
-
     def embed(
         self,
         texts: list[str] | None = None,
         images: list[bytes] | None = None,
     ) -> list[np.ndarray]:
-        """Generate embeddings via llama.cpp /embeddings endpoint.
+        """Generate embeddings via llama.cpp /v1/embeddings endpoint.
 
-        Supports batch requests. Uses content format with prompt_string and
+        Each text or image is one entry in `input`, using the chat-style
-        multimodal_data for images (PR #15108). Server must be started with
+        content array from ggml-org/llama.cpp#29556. Server must be started
-        --embeddings and --mmproj for multimodal support.
+        with --embeddings, and --mmproj for image support.
         """
         if self.provider is None:
             logger.warning(
@@ -843,49 +801,42 @@ class LlamaCppClient(GenAIClient):
 
         EMBEDDING_DIM = 768
 
-        encoded_images: list[str] = []
+        inputs: list[dict[str, Any]] = [
+            {"content": [{"type": "text", "text": text}]} for text in texts
+        ]
+
         for img in images:
             # llama.cpp uses STB which does not support WebP; convert to JPEG
             jpeg_bytes = _to_jpeg(img)
             to_encode = jpeg_bytes if jpeg_bytes is not None else img
-            encoded_images.append(base64.b64encode(to_encode).decode("utf-8"))
+            encoded = base64.b64encode(to_encode).decode("utf-8")
-
+            # The trailing newline keeps tokenization identical to the older
-        def build_content() -> list[dict[str, Any]]:
+            # "<__media__>\n" prompt_string format, so indexed vectors stay valid
-            # prompt_string must contain the server's media marker placeholder
+            inputs.append(
-            # for each image. The marker is randomized per server startup.
-            content: list[dict[str, Any]] = []
-            for text in texts:
-                content.append({"prompt_string": text})
-            for encoded in encoded_images:
-                content.append(
                 {
-                        "prompt_string": f"{self._media_marker}\n",
+                    "content": [
-                        "multimodal_data": [encoded],
+                        {
+                            "type": "image_url",
+                            "image_url": {"url": f"data:image/jpeg;base64,{encoded}"},
+                        },
+                        {"type": "text", "text": "\n"},
+                    ]
                 }
             )
-            return content
 
-        def post_embeddings() -> requests.Response:
+        try:
-            return self._post(
+            response = self._post(
-                f"{self.provider}/embeddings",
+                f"{self.provider}/v1/embeddings",
-                json={"model": self.genai_config.model, "content": build_content()},
+                json={
+                    "model": self.genai_config.model,
+                    "input": inputs,
+                    "encoding_format": "float",
+                },
                 timeout=self.timeout,
             )
-
-        try:
-            try:
-                response = post_embeddings()
             response.raise_for_status()
-            except requests.exceptions.RequestException:
+            items = response.json().get("data")
-                # The server may have restarted with a new media marker.
-                # Refresh from /props; only retry if the marker actually changed.
-                if not encoded_images or not self._refresh_media_marker():
-                    raise
-                response = post_embeddings()
-                response.raise_for_status()
-            result = response.json()
 
-            items = result.get("data", result) if isinstance(result, dict) else result
             if not isinstance(items, list):
                 logger.warning("llama.cpp embeddings returned unexpected format")
                 return []
@@ -896,11 +847,7 @@ class LlamaCppClient(GenAIClient):
                 if emb is None:
                     logger.warning("llama.cpp embeddings item missing embedding field")
                     continue
-                arr = np.array(emb, dtype=np.float32)
+                arr = np.array(emb, dtype=np.float32).flatten()
-                if arr.ndim > 1:
-                    # llama.cpp can return token-level embeddings; pool per item
-                    arr = arr.mean(axis=0)
-                arr = arr.flatten()
                 orig_dim = arr.size
                 if orig_dim != EMBEDDING_DIM:
                     if orig_dim > EMBEDDING_DIM:

frigate/genai/prompts.py

@@ -104,6 +104,21 @@ def build_review_description_prompt(
         else:
             return "\n- (No objects detected)"
 
+    def get_state_changes_section() -> str:
+        # empty when nothing changed so the prompt is otherwise unaffected
+        changes = review_data.get("classification_state_changes")
+
+        if not changes:
+            return ""
+
+        return (
+            "\n\n## State Changes\n\n"
+            "The camera's state classifiers watch fixed areas of the scene and "
+            "reported these changes. They come from the classifiers rather than "
+            "from the images, and they are reliable. Describe each one where it "
+            "fits in the sequence of events.\n- " + "\n- ".join(changes)
+        )
+
     fields = get_review_field_guidelines(response_style)
     frame_guidance = f"\n{FRAME_ANNOTATION_GUIDANCE}" if frame_captions else ""
 
@@ -145,7 +160,7 @@ Respond with a JSON object matching the provided schema. Field-specific guidance
 - Camera: {review_data["camera"]}
 - Total frames: {len(thumbnails)} (Frame 1 = earliest, Frame {len(thumbnails)} = latest){frame_guidance}
 - Activity started at {review_data["start"]} and lasted {review_data["duration"]} seconds
-- Zones involved: {", ".join(review_data["zones"]) if review_data["zones"] else "None"}
+- Zones involved: {", ".join(review_data["zones"]) if review_data["zones"] else "None"}{get_state_changes_section()}
 
 ## Objects in Scene
 
@@ -196,6 +211,17 @@ def build_review_summary_prompt(
         f" to "
         f"{datetime.datetime.fromtimestamp(end_ts).strftime('%B %d, %Y at %I:%M %p')}"
     )
+    has_state_changes = any(
+        "state_changes" in item
+        for event in events
+        for item in [event, *event.get("context", [])]
+    )
+    state_changes_format = (
+        '\n- "state_changes" (only on some events): changes to monitored areas '
+        "reported by the camera's state classifiers, which are reliable"
+        if has_state_changes
+        else ""
+    )
     prompt = f"""
 You are a security officer writing a concise security report.
 
@@ -203,7 +229,7 @@ Time range: {time_range}
 
 Input format: Each event is a JSON object with:
 - "title", "scene", "confidence", "potential_threat_level" (0-2), "other_concerns", "camera", "time", "start_time", "end_time"
-- "context": array of related events from other cameras that occurred during overlapping time periods
+- "context": array of related events from other cameras that occurred during overlapping time periods{state_changes_format}
 
 **Note: Use the "scene" field for event descriptions in the report. Ignore any "shortSummary" field if present.**
 

frigate/motion/improved_motion.py

@@ -19,6 +19,7 @@ class ImprovedMotionDetector(MotionDetector):
         config: RuntimeMotionConfig,
         fps: int,
         ptz_metrics: PTZMetrics | None = None,
+        autotracking_enabled: bool = False,
         name: str = "improved",
         blur_radius: int = 1,
         interpolation: int = cv2.INTER_NEAREST,
@@ -45,6 +46,7 @@ class ImprovedMotionDetector(MotionDetector):
         self.contrast_values[:, 1:2] = 255
         self.contrast_values_index = 0
         self.ptz_metrics = ptz_metrics
+        self.autotracking_enabled = autotracking_enabled
         self.last_stop_time: float | None = None
 
     def is_calibrating(self) -> bool:
@@ -59,8 +61,7 @@ class ImprovedMotionDetector(MotionDetector):
         # if ptz motor is moving from autotracking, quickly return
         # a single box that is 80% of the frame
         if self.ptz_metrics is not None and (
-            self.ptz_metrics.autotracker_enabled.value
+            self.autotracking_enabled and not self.ptz_metrics.motor_stopped.is_set()
-            and not self.ptz_metrics.motor_stopped.is_set()
         ):
             return [
                 (
@@ -162,7 +163,7 @@ class ImprovedMotionDetector(MotionDetector):
         # if so, reassign the average to the current frame so we begin with a new baseline
         if self.ptz_metrics is not None and (
             # ensure we only do this for cameras with autotracking enabled
-            self.ptz_metrics.autotracker_enabled.value
+            self.autotracking_enabled
             and self.ptz_metrics.motor_stopped.is_set()
             and (
                 self.last_stop_time is None

frigate/plus.py

@@ -9,7 +9,9 @@ from typing import Any
 import cv2
 import requests
 from numpy import ndarray
+from requests.adapters import HTTPAdapter
 from requests.models import Response
+from urllib3.util.retry import Retry
 
 from frigate.const import MODEL_CACHE_DIR, PLUS_API_HOST, PLUS_ENV_VAR
 
@@ -101,6 +103,13 @@ class PlusApi:
         self._is_active: bool = self.key is not None
         self._token_data: dict = {}
 
+        # Retry connection failures so a network that comes up late at startup
+        # doesn't fail the Frigate+ model download
+        self._session = requests.Session()
+        self._session.mount(
+            self.host, HTTPAdapter(max_retries=Retry(connect=5, backoff_factor=1))
+        )
+
     def _refresh_token_if_needed(self) -> None:
         if (
             self._token_data.get("expires") is None
@@ -111,7 +120,9 @@ class PlusApi:
                     "Plus API key not set. See https://docs.frigate.video/integrations/plus#set-your-api-key"
                 )
             parts = self.key.split(":")
-            r = requests.get(f"{self.host}/v1/auth/token", auth=(parts[0], parts[1]))
+            r = self._session.get(
+                f"{self.host}/v1/auth/token", auth=(parts[0], parts[1])
+            )
             if not r.ok:
                 raise Exception(f"Unable to refresh API token: {r.text}")
             self._token_data = r.json()
@@ -121,19 +132,19 @@ class PlusApi:
         return {"authorization": f"Bearer {self._token_data.get('accessToken')}"}
 
     def _get(self, path: str) -> Response:
-        return requests.get(
+        return self._session.get(
             f"{self.host}/v1/{path}", headers=self._get_authorization_header()
         )
 
     def _post(self, path: str, data: dict) -> Response:
-        return requests.post(
+        return self._session.post(
             f"{self.host}/v1/{path}",
             headers=self._get_authorization_header(),
             json=data,
         )
 
     def _put(self, path: str, data: dict) -> Response:
-        return requests.put(
+        return self._session.put(
             f"{self.host}/v1/{path}",
             headers=self._get_authorization_header(),
             json=data,

frigate/ptz/autotrack.py

@@ -23,6 +23,7 @@ from frigate.config import CameraConfig, FrigateConfig, ZoomingModeEnum
 from frigate.config.camera.updater import (
     CameraConfigUpdateEnum,
     CameraConfigUpdateSubscriber,
+    CameraConfigUpdateTopic,
 )
 from frigate.const import (
     AUTOTRACKING_MAX_AREA_RATIO,
@@ -42,6 +43,11 @@ from frigate.util.image import SharedMemoryFrameManager, intersection_over_union
 logger = logging.getLogger(__name__)
 
 
+def calculate_max_target_box(zoom_factor: float) -> float:
+    """Return the largest target box ratio allowed for a zoom factor."""
+    return AUTOTRACKING_MAX_AREA_RATIO ** (1 / zoom_factor)
+
+
 def ptz_moving_at_frame_time(frame_time, ptz_start_time, ptz_stop_time):
     # Determine if the PTZ was in motion at the set frame time
     # for non ptz/autotracking cameras, this will always return False
@@ -180,7 +186,7 @@ class PtzMotionEstimator:
         return self.coord_transformations
 
 
-class PtzAutoTrackerThread(threading.Thread):
+class PtzAutoTracker(threading.Thread):
     def __init__(
         self,
         config: FrigateConfig,
@@ -190,56 +196,14 @@ class PtzAutoTrackerThread(threading.Thread):
         stop_event: MpEvent,
     ) -> None:
         super().__init__(name="ptz_autotracker")
-        self.ptz_autotracker = PtzAutoTracker(
-            config, onvif, ptz_metrics, dispatcher, stop_event
-        )
-        self.stop_event = stop_event
-        self.config = config
-
-    def run(self):
-        while not self.stop_event.wait(1):
-            self.ptz_autotracker.check_for_updates()
-
-            for camera, camera_config in list(self.config.cameras.items()):
-                if not camera_config.enabled:
-                    continue
-
-                if camera_config.onvif.autotracking.enabled:
-                    future = asyncio.run_coroutine_threadsafe(
-                        self.ptz_autotracker.camera_maintenance(camera),
-                        self.ptz_autotracker.onvif.loop,
-                    )
-                    # Wait for the coroutine to complete
-                    future.result()
-                else:
-                    # disabled dynamically by mqtt
-                    if self.ptz_autotracker.tracked_object.get(camera):
-                        self.ptz_autotracker.tracked_object[camera] = None
-                        self.ptz_autotracker.tracked_object_history[camera].clear()
-
-        self.ptz_autotracker.config_subscriber.stop()
-        logger.info("Exiting autotracker...")
-
-
-class PtzAutoTracker:
-    def __init__(
-        self,
-        config: FrigateConfig,
-        onvif: OnvifController,
-        ptz_metrics: PTZMetrics,
-        dispatcher: Dispatcher,
-        stop_event: MpEvent,
-    ) -> None:
         self.config = config
         self.onvif = onvif
         self.ptz_metrics = ptz_metrics
         self.dispatcher = dispatcher
         self.stop_event = stop_event
-        self.tracked_object: dict[str, object] = {}
+        self.tracked_object: dict[str, TrackedObject | None] = {}
         self.tracked_object_history: dict[str, object] = {}
-        self.tracked_object_metrics: dict[str, object] = {}
+        self.tracked_object_metrics: dict[str, dict[str, Any]] = {}
-        self.object_types: dict[str, object] = {}
-        self.required_zones: dict[str, object] = {}
         self.move_queues: dict[str, object] = {}
         self.move_queue_locks: dict[str, object] = {}
         self.move_threads: dict[str, object] = {}
@@ -249,7 +213,6 @@ class PtzAutoTracker:
         self.intercept: dict[str, object] = {}
         self.move_coefficients: dict[str, object] = {}
         self.zoom_time: dict[str, float] = {}
-        self.zoom_factor: dict[str, object] = {}
 
         self.config_subscriber = CameraConfigUpdateSubscriber(
             self.config,
@@ -277,44 +240,37 @@ class PtzAutoTracker:
                 # Wait for the coroutine to complete
                 future.result()
 
-    def check_for_updates(self) -> None:
+    def run(self) -> None:
-        """Apply camera config updates and mirror autotracking state to ptz metrics.
+        while not self.stop_event.wait(1):
+            self.config_subscriber.check_for_updates()
 
-        The camera processes read autotracker_enabled rather than the config, so it
+            for camera, camera_config in list(self.config.cameras.items()):
-        has to follow every path that can change autotracking, not just the mqtt
+                if not camera_config.enabled:
-        toggle that writes it directly.
-        """
-        updates = self.config_subscriber.check_for_updates()
-
-        for cameras in updates.values():
-            for camera in cameras:
-                camera_config = self.config.cameras.get(camera)
-                metrics = self.ptz_metrics.get(camera)
-
-                # a camera added at runtime gets its metrics from the maintainer on
-                # another thread, which seeds them from this same config value
-                if camera_config is None or metrics is None:
                     continue
 
-                metrics.autotracker_enabled.value = (
+                if camera_config.onvif.autotracking.enabled:
-                    camera_config.onvif.autotracking.enabled
+                    future = asyncio.run_coroutine_threadsafe(
+                        self.camera_maintenance(camera), self.onvif.loop
                     )
+                    # Wait for the coroutine to complete
+                    future.result()
+                else:
+                    # disabled dynamically by mqtt
+                    if self.tracked_object.get(camera):
+                        self.tracked_object[camera] = None
+                        self.tracked_object_history[camera].clear()
+
+        self.config_subscriber.stop()
+        logger.info("Exiting autotracker...")
 
     async def _autotracker_setup(self, camera_config: CameraConfig, camera: str):
         logger.debug(f"{camera}: Autotracker init")
 
-        self.object_types[camera] = camera_config.onvif.autotracking.track
-        self.required_zones[camera] = camera_config.onvif.autotracking.required_zones
-        self.zoom_factor[camera] = camera_config.onvif.autotracking.zoom_factor
-
         self.tracked_object[camera] = None
         self.tracked_object_history[camera] = deque(
             maxlen=round(camera_config.detect.fps * 1.5)
         )
-        self.tracked_object_metrics[camera] = {
+        self._reset_tracked_object_metrics(camera)
-            "max_target_box": AUTOTRACKING_MAX_AREA_RATIO
-            ** (1 / self.zoom_factor[camera])
-        }
 
         self.calibrating[camera] = False
         self.move_metrics[camera] = []
@@ -326,43 +282,22 @@ class PtzAutoTracker:
 
         # handle onvif constructor failing due to no connection
         if camera not in self.onvif.cams:
-            logger.warning(
+            self._disable(camera, "onvif connection failed")
-                f"Disabling autotracking for {camera}: onvif connection failed"
-            )
-            camera_config.onvif.autotracking.enabled = False
-            self.ptz_metrics[camera].autotracker_enabled.value = False
             return
 
         if not self.onvif.cams[camera]["init"]:
             if not await self.onvif._init_onvif(camera):
-                logger.warning(
+                self._disable(camera, "Unable to initialize onvif")
-                    f"Disabling autotracking for {camera}: Unable to initialize onvif"
-                )
-                camera_config.onvif.autotracking.enabled = False
-                self.ptz_metrics[camera].autotracker_enabled.value = False
                 return
 
             if "pt-r-fov" not in self.onvif.cams[camera]["features"]:
-                logger.warning(
+                self._disable(camera, "FOV relative movement not supported")
-                    f"Disabling autotracking for {camera}: FOV relative movement not supported"
-                )
-                camera_config.onvif.autotracking.enabled = False
-                self.ptz_metrics[camera].autotracker_enabled.value = False
                 return
 
             move_status_supported = await self.onvif.get_service_capabilities(camera)
 
-            if not (
+            if str(move_status_supported).lower() != "true":
-                isinstance(move_status_supported, bool) and move_status_supported
+                self._disable(camera, "ONVIF MoveStatus not supported")
-            ) and not (
-                isinstance(move_status_supported, str)
-                and move_status_supported.lower() == "true"
-            ):
-                logger.warning(
-                    f"Disabling autotracking for {camera}: ONVIF MoveStatus not supported"
-                )
-                camera_config.onvif.autotracking.enabled = False
-                self.ptz_metrics[camera].autotracker_enabled.value = False
                 return
 
         if self.onvif.cams[camera]["init"]:
@@ -374,59 +309,41 @@ class PtzAutoTracker:
             )
 
             if camera_config.onvif.autotracking.movement_weights:
-                if len(camera_config.onvif.autotracking.movement_weights) == 6:
+                (
-                    camera_config.onvif.autotracking.movement_weights = [
+                    self.ptz_metrics[camera].min_zoom.value,
-                        float(val)
+                    self.ptz_metrics[camera].max_zoom.value,
-                        for val in camera_config.onvif.autotracking.movement_weights
+                    self.intercept[camera],
-                    ]
+                    *self.move_coefficients[camera],
-                    self.ptz_metrics[
+                    self.zoom_time[camera],
-                        camera
+                ) = map(float, camera_config.onvif.autotracking.movement_weights)
-                    ].min_zoom.value = (
-                        camera_config.onvif.autotracking.movement_weights[0]
-                    )
-                    self.ptz_metrics[
-                        camera
-                    ].max_zoom.value = (
-                        camera_config.onvif.autotracking.movement_weights[1]
-                    )
-                    self.intercept[camera] = (
-                        camera_config.onvif.autotracking.movement_weights[2]
-                    )
-                    self.move_coefficients[camera] = (
-                        camera_config.onvif.autotracking.movement_weights[3:5]
-                    )
-                    self.zoom_time[camera] = (
-                        camera_config.onvif.autotracking.movement_weights[5]
-                    )
-                else:
-                    camera_config.onvif.autotracking.enabled = False
-                    self.ptz_metrics[camera].autotracker_enabled.value = False
-                    logger.warning(
-                        f"Autotracker recalibration is required for {camera}. Disabling autotracking."
-                    )
 
             if camera_config.onvif.autotracking.calibrate_on_startup:
                 await self._calibrate_camera(camera)
 
-        self.ptz_metrics[camera].tracking_active.clear()
         self.dispatcher.publish(f"{camera}/ptz_autotracker/active", "OFF", retain=False)
         self.autotracker_init[camera] = True
 
-    def _write_config(self, camera):
+    def _disable(self, camera: str, reason: str) -> None:
-        config_file = find_config_file()
+        logger.warning(f"Disabling autotracking for {camera}: {reason}")
+        autotracking_config = self.config.cameras[camera].onvif.autotracking
+        autotracking_config.enabled = False
 
-        logger.debug(
+        # the camera process holds its own copy of the config
-            f"{camera}: Writing new config with autotracker motion coefficients: {self.config.cameras[camera].onvif.autotracking.movement_weights}"
+        self.dispatcher.config_updater.publish_update(
+            CameraConfigUpdateTopic(CameraConfigUpdateEnum.autotracking, camera),
+            autotracking_config,
         )
 
-        update_yaml_file_bulk(
+    def _reset_tracked_object_metrics(self, camera: str) -> None:
-            config_file,
+        self.tracked_object_metrics[camera] = {}
-            {
+
-                f"cameras.{camera}.onvif.autotracking.movement_weights": self.config.cameras[
+    async def _wait_until_stopped(
-                    camera
+        self, camera: str, metrics: PTZMetrics | None = None
-                ].onvif.autotracking.movement_weights
+    ) -> None:
-            },
+        metrics = metrics or self.ptz_metrics[camera]
-        )
+
+        while not metrics.motor_stopped.is_set():
+            await self.onvif.get_camera_status(camera)
 
     async def _calibrate_camera(self, camera):
         # move the camera from the preset in steps and measure the time it takes to move that amount
@@ -459,8 +376,7 @@ class PtzAutoTracker:
                     1,
                 )
 
-                while not self.ptz_metrics[camera].motor_stopped.is_set():
+                await self._wait_until_stopped(camera)
-                    await self.onvif.get_camera_status(camera)
 
                 zoom_out_values.append(self.ptz_metrics[camera].zoom_level.value)
 
@@ -470,8 +386,7 @@ class PtzAutoTracker:
                     1,
                 )
 
-                while not self.ptz_metrics[camera].motor_stopped.is_set():
+                await self._wait_until_stopped(camera)
-                    await self.onvif.get_camera_status(camera)
 
                 zoom_in_values.append(self.ptz_metrics[camera].zoom_level.value)
 
@@ -488,8 +403,7 @@ class PtzAutoTracker:
                         1,
                     )
 
-                    while not self.ptz_metrics[camera].motor_stopped.is_set():
+                    await self._wait_until_stopped(camera)
-                        await self.onvif.get_camera_status(camera)
 
                     zoom_out_values.append(self.ptz_metrics[camera].zoom_level.value)
 
@@ -503,8 +417,7 @@ class PtzAutoTracker:
                         1,
                     )
 
-                    while not self.ptz_metrics[camera].motor_stopped.is_set():
+                    await self._wait_until_stopped(camera)
-                        await self.onvif.get_camera_status(camera)
 
                     zoom_stop_time = time.time()
 
@@ -518,8 +431,7 @@ class PtzAutoTracker:
                         1,
                     )
 
-                    while not self.ptz_metrics[camera].motor_stopped.is_set():
+                    await self._wait_until_stopped(camera)
-                        await self.onvif.get_camera_status(camera)
 
                     full_relative_stop_time = time.time()
 
@@ -531,8 +443,7 @@ class PtzAutoTracker:
                         1,
                     )
 
-                    while not self.ptz_metrics[camera].motor_stopped.is_set():
+                    await self._wait_until_stopped(camera)
-                        await self.onvif.get_camera_status(camera)
 
                     self.zoom_time[camera] = (
                         full_relative_stop_time - full_relative_start_time
@@ -558,9 +469,7 @@ class PtzAutoTracker:
         self.ptz_metrics[camera].reset.set()
         self.ptz_metrics[camera].motor_stopped.clear()
 
-        # Wait until the camera finishes moving
+        await self._wait_until_stopped(camera)
-        while not self.ptz_metrics[camera].motor_stopped.is_set():
-            await self.onvif.get_camera_status(camera)
 
         for step in range(num_steps):
             pan = step_sizes[step]
@@ -569,9 +478,7 @@ class PtzAutoTracker:
             start_time = time.time()
             await self.onvif._move_relative(camera, pan, tilt, 0, 1)
 
-            # Wait until the camera finishes moving
+            await self._wait_until_stopped(camera)
-            while not self.ptz_metrics[camera].motor_stopped.is_set():
-                await self.onvif.get_camera_status(camera)
             stop_time = time.time()
 
             self.move_metrics[camera].append(
@@ -590,9 +497,7 @@ class PtzAutoTracker:
             self.ptz_metrics[camera].reset.set()
             self.ptz_metrics[camera].motor_stopped.clear()
 
-            # Wait until the camera finishes moving
+            await self._wait_until_stopped(camera)
-            while not self.ptz_metrics[camera].motor_stopped.is_set():
-                await self.onvif.get_camera_status(camera)
 
             logger.info(
                 f"Calibration for {camera} in progress: {round((step / num_steps) * 100)}% complete"
@@ -668,7 +573,14 @@ class PtzAutoTracker:
                 f"{camera}: New regression parameters - intercept: {self.intercept[camera]}, coefficients: {self.move_coefficients[camera]}"
             )
 
-            self._write_config(camera)
+            update_yaml_file_bulk(
+                find_config_file(),
+                {
+                    f"cameras.{camera}.onvif.autotracking.movement_weights": self.config.cameras[
+                        camera
+                    ].onvif.autotracking.movement_weights
+                },
+            )
 
     def _predict_movement_time(self, camera, pan, tilt):
         combined_movement = abs(pan) + abs(tilt)
@@ -682,6 +594,18 @@ class PtzAutoTracker:
             [self.tracked_object_history[camera][-1]["frame_time"] + time],
         )
 
+    def _predict_target_box(self, camera, predicted_time):
+        target_box = self.tracked_object_metrics[camera]["target_box"]
+
+        if not predicted_time:
+            return target_box
+
+        frame_shape = self.config.cameras[camera].frame_shape
+
+        return target_box + self._predict_area_after_time(camera, predicted_time) / (
+            frame_shape[0] * frame_shape[1]
+        )
+
     def _calculate_tracked_object_metrics(self, camera, obj):
         def remove_outliers(data):
             areas = [item["area"] for item in data]
@@ -703,19 +627,20 @@ class PtzAutoTracker:
             return filtered_data
 
         camera_config = self.config.cameras[camera]
+        tom = self.tracked_object_metrics[camera]
+        zoom_factor = camera_config.onvif.autotracking.zoom_factor
         camera_width = camera_config.frame_shape[1]
         camera_height = camera_config.frame_shape[0]
 
         # Extract areas and calculate weighted average
         # grab the largest dimension of the bounding box and create a square from that
-        # Filter out the initial frame and use a recent time window
+        # Use a recent time window
         current_time = obj.obj_data["frame_time"]
         time_window = 1.5  # seconds
         history = [
             entry
             for entry in self.tracked_object_history[camera]
-            if not entry.get("is_initial_frame", False)
+            if current_time - entry["frame_time"] <= time_window
-            and current_time - entry["frame_time"] <= time_window
         ]
         if not history:  # Fallback to latest if no recent entries
             history = [self.tracked_object_history[camera][-1]]
@@ -748,33 +673,29 @@ class PtzAutoTracker:
             )
             y = np.array([item["area"] for item in filtered_areas_not_touching_edge])
 
-            self.tracked_object_metrics[camera]["area_coefficients"] = np.linalg.lstsq(
+            tom["area_coefficients"] = np.linalg.lstsq(X.reshape(-1, 1), y, rcond=None)[
-                X.reshape(-1, 1), y, rcond=None
+                0
-            )[0]
+            ]
         else:
-            self.tracked_object_metrics[camera]["area_coefficients"] = np.array([0])
+            tom["area_coefficients"] = np.array([0])
 
         weights = np.arange(1, len(filtered_areas) + 1)
         weighted_area = np.average(
             [item["area"] for item in filtered_areas], weights=weights
         )
 
-        self.tracked_object_metrics[camera]["target_box"] = (
+        tom["target_box"] = (
             weighted_area / (camera_width * camera_height)
-        ) ** self.zoom_factor[camera]
+        ) ** zoom_factor
 
-        if "original_target_box" not in self.tracked_object_metrics[camera]:
+        if "original_target_box" not in tom:
-            self.tracked_object_metrics[camera]["original_target_box"] = (
+            tom["original_target_box"] = tom["target_box"]
-                self.tracked_object_metrics[camera]["target_box"]
-            )
 
         (
-            self.tracked_object_metrics[camera]["valid_velocity"],
+            tom["valid_velocity"],
-            self.tracked_object_metrics[camera]["velocity"],
+            tom["velocity"],
         ) = self._get_valid_velocity(camera, obj)
-        self.tracked_object_metrics[camera]["distance"] = self._get_distance_threshold(
+        tom["distance"] = self._get_distance_threshold(camera, obj)
-            camera, obj
-        )
 
         centroid_distance = np.linalg.norm(
             [
@@ -785,9 +706,7 @@ class PtzAutoTracker:
 
         logger.debug(f"{camera}: Centroid distance: {centroid_distance}")
 
-        self.tracked_object_metrics[camera]["below_distance_threshold"] = (
+        tom["below_distance_threshold"] = centroid_distance < tom["distance"]
-            centroid_distance < self.tracked_object_metrics[camera]["distance"]
-        )
 
     async def _process_move_queue(self, camera):
         move_queue = self.move_queues[camera]
@@ -833,16 +752,12 @@ class PtzAutoTracker:
                         if pan != 0 or tilt != 0:
                             await self.onvif._move_relative(camera, pan, tilt, 0, 1)
 
-                            # Wait until the camera finishes moving
+                            await self._wait_until_stopped(camera, metrics)
-                            while not metrics.motor_stopped.is_set():
-                                await self.onvif.get_camera_status(camera)
 
                         if zoom > 0 and metrics.zoom_level.value != zoom:
                             await self.onvif._zoom_absolute(camera, zoom, 1)
 
-                    # Wait until the camera finishes moving
+                    await self._wait_until_stopped(camera, metrics)
-                    while not metrics.motor_stopped.is_set():
-                        await self.onvif.get_camera_status(camera)
 
                     if camera_config.onvif.autotracking.movement_weights:
                         logger.debug(
@@ -878,42 +793,21 @@ class PtzAutoTracker:
             await move_queue.get()
 
     def _enqueue_move(self, camera, frame_time, pan, tilt, zoom):
-        def split_value(value, suppress_diff=True):
+        pan, tilt, zoom = (np.clip(value, -1, 1) for value in (pan, tilt, zoom))
-            clipped = np.clip(value, -1, 1)
-
-            # don't make small movements
-            if -0.05 < clipped < 0.05 and suppress_diff:
-                diff = 0.0
-            else:
-                diff = value - clipped
-
-            return clipped, diff
 
         if (
-            frame_time > self.ptz_metrics[camera].start_time.value
+            (pan != 0 or tilt != 0 or zoom != 0)
+            and frame_time > self.ptz_metrics[camera].start_time.value
             and frame_time > self.ptz_metrics[camera].stop_time.value
             and not self.move_queue_locks[camera].locked()
         ):
-            # we can split up any large moves caused by velocity estimated movements if necessary
-            # get an excess amount and assign it instead of 0 below
-            while pan != 0 or tilt != 0 or zoom != 0:
-                pan, _ = split_value(pan)
-                tilt, _ = split_value(tilt)
-                zoom, _ = split_value(zoom, False)
-
             logger.debug(
                 f"{camera}: Enqueue movement for frame time: {frame_time} pan: {pan}, tilt: {tilt}, zoom: {zoom}"
             )
-                move_data = (frame_time, pan, tilt, zoom)
             self.onvif.loop.call_soon_threadsafe(
-                    self.move_queues[camera].put_nowait, move_data
+                self.move_queues[camera].put_nowait, (frame_time, pan, tilt, zoom)
             )
 
-                # reset values to not split up large movements
-                pan = 0
-                tilt = 0
-                zoom = 0
-
     def _touching_frame_edges(self, camera, box):
         camera_config = self.config.cameras[camera]
         camera_width = camera_config.frame_shape[1]
@@ -1046,16 +940,17 @@ class PtzAutoTracker:
 
         return distance_threshold
 
-    def _should_zoom_in(
+    def _should_zoom_in(self, camera: str, box, predicted_time):
-        self, camera: str, obj: TrackedObject, box, predicted_time, debug_zooming=False
-    ):
         # returns True if we should zoom in, False if we should zoom out, None to do nothing
         camera_config = self.config.cameras[camera]
+        tom = self.tracked_object_metrics[camera]
+        zoom_factor = camera_config.onvif.autotracking.zoom_factor
+        max_target_box = calculate_max_target_box(zoom_factor)
         camera_width = camera_config.frame_shape[1]
         camera_height = camera_config.frame_shape[0]
         camera_fps = camera_config.detect.fps
 
-        average_velocity = self.tracked_object_metrics[camera]["velocity"]
+        average_velocity = tom["velocity"]
 
         bb_left, bb_top, bb_right, bb_bottom = box
 
@@ -1073,13 +968,11 @@ class PtzAutoTracker:
         touching_frame_edges = self._touching_frame_edges(camera, box)
 
         # make sure object is centered in the frame
-        below_distance_threshold = self.tracked_object_metrics[camera][
+        below_distance_threshold = tom["below_distance_threshold"]
-            "below_distance_threshold"
-        ]
 
         below_dimension_threshold = (bb_right - bb_left) <= camera_width * (
-            self.zoom_factor[camera] + 0.1
+            zoom_factor + 0.1
-        ) and (bb_bottom - bb_top) <= camera_height * (self.zoom_factor[camera] + 0.1)
+        ) and (bb_bottom - bb_top) <= camera_height * (zoom_factor + 0.1)
 
         # ensure object is not moving quickly
         below_velocity_threshold = np.all(
@@ -1087,30 +980,16 @@ class PtzAutoTracker:
             < np.tile([velocity_threshold_x, velocity_threshold_y], 2)
         ) or np.all(average_velocity == 0)
 
-        if not predicted_time:
+        calculated_target_box = self._predict_target_box(camera, predicted_time)
-            calculated_target_box = self.tracked_object_metrics[camera]["target_box"]
-        else:
-            calculated_target_box = self.tracked_object_metrics[camera][
-                "target_box"
-            ] + self._predict_area_after_time(camera, predicted_time) / (
-                camera_width * camera_height
-            )
 
-        below_area_threshold = (
+        below_area_threshold = calculated_target_box < max_target_box
-            calculated_target_box
-            < self.tracked_object_metrics[camera]["max_target_box"]
-        )
 
         # introduce some hysteresis to prevent a yo-yo zooming effect
         zoom_out_hysteresis = (
-            calculated_target_box
+            calculated_target_box > max_target_box * AUTOTRACKING_ZOOM_OUT_HYSTERESIS
-            > self.tracked_object_metrics[camera]["max_target_box"]
-            * AUTOTRACKING_ZOOM_OUT_HYSTERESIS
         )
         zoom_in_hysteresis = (
-            calculated_target_box
+            calculated_target_box < max_target_box * AUTOTRACKING_ZOOM_IN_HYSTERESIS
-            < self.tracked_object_metrics[camera]["max_target_box"]
-            * AUTOTRACKING_ZOOM_IN_HYSTERESIS
         )
 
         at_max_zoom = (
@@ -1122,8 +1001,6 @@ class PtzAutoTracker:
             == self.ptz_metrics[camera].min_zoom.value
         )
 
-        # debug zooming
-        if debug_zooming:
         logger.debug(
             f"{camera}: Zoom test: touching edges: count: {touching_frame_edges} left: {bb_left < AUTOTRACKING_ZOOM_EDGE_THRESHOLD * camera_width}, right: {bb_right > (1 - AUTOTRACKING_ZOOM_EDGE_THRESHOLD) * camera_width}, top: {bb_top < AUTOTRACKING_ZOOM_EDGE_THRESHOLD * camera_height}, bottom: {bb_bottom > (1 - AUTOTRACKING_ZOOM_EDGE_THRESHOLD) * camera_height}"
         )
@@ -1131,10 +1008,10 @@ class PtzAutoTracker:
             f"{camera}: Zoom test: below distance threshold: {(below_distance_threshold)}"
         )
         logger.debug(
-                f"{camera}: Zoom test: below area threshold: {(below_area_threshold)} target: {self.tracked_object_metrics[camera]['target_box']}, calculated: {calculated_target_box}, max: {self.tracked_object_metrics[camera]['max_target_box']}"
+            f"{camera}: Zoom test: below area threshold: {(below_area_threshold)} target: {tom['target_box']}, calculated: {calculated_target_box}, max: {max_target_box}"
         )
         logger.debug(
-                f"{camera}: Zoom test: below dimension threshold: {below_dimension_threshold} width: {bb_right - bb_left}, max width: {camera_width * (self.zoom_factor[camera] + 0.1)}, height: {bb_bottom - bb_top}, max height: {camera_height * (self.zoom_factor[camera] + 0.1)}"
+            f"{camera}: Zoom test: below dimension threshold: {below_dimension_threshold} width: {bb_right - bb_left}, max width: {camera_width * (zoom_factor + 0.1)}, height: {bb_bottom - bb_top}, max height: {camera_height * (zoom_factor + 0.1)}"
         )
         logger.debug(
             f"{camera}: Zoom test: below velocity threshold: {below_velocity_threshold} velocity x: {abs(average_velocity[0])}, x threshold: {velocity_threshold_x}, velocity y: {abs(average_velocity[1])}, y threshold: {velocity_threshold_y}"
@@ -1142,10 +1019,10 @@ class PtzAutoTracker:
         logger.debug(f"{camera}: Zoom test: at max zoom: {at_max_zoom}")
         logger.debug(f"{camera}: Zoom test: at min zoom: {at_min_zoom}")
         logger.debug(
-                f"{camera}: Zoom test: zoom in hysteresis limit: {zoom_in_hysteresis} value: {AUTOTRACKING_ZOOM_IN_HYSTERESIS} original: {self.tracked_object_metrics[camera]['original_target_box']} max: {self.tracked_object_metrics[camera]['max_target_box']} target: {calculated_target_box if calculated_target_box else self.tracked_object_metrics[camera]['target_box']}"
+            f"{camera}: Zoom test: zoom in hysteresis limit: {zoom_in_hysteresis} value: {AUTOTRACKING_ZOOM_IN_HYSTERESIS} original: {tom['original_target_box']} max: {max_target_box} target: {calculated_target_box if calculated_target_box else tom['target_box']}"
         )
         logger.debug(
-                f"{camera}: Zoom test: zoom out hysteresis limit: {zoom_out_hysteresis} value: {AUTOTRACKING_ZOOM_OUT_HYSTERESIS} original: {self.tracked_object_metrics[camera]['original_target_box']} max: {self.tracked_object_metrics[camera]['max_target_box']} target: {calculated_target_box if calculated_target_box else self.tracked_object_metrics[camera]['target_box']}"
+            f"{camera}: Zoom test: zoom out hysteresis limit: {zoom_out_hysteresis} value: {AUTOTRACKING_ZOOM_OUT_HYSTERESIS} original: {tom['original_target_box']} max: {max_target_box} target: {calculated_target_box if calculated_target_box else tom['target_box']}"
         )
 
         # Zoom in conditions (and)
@@ -1237,7 +1114,7 @@ class PtzAutoTracker:
             )
 
         zoom = self._get_zoom_amount(
-            camera, obj, predicted_box, predicted_movement_time, debug_zoom=True
+            camera, obj, predicted_box, predicted_movement_time
         )
 
         if (
@@ -1298,9 +1175,11 @@ class PtzAutoTracker:
         obj: TrackedObject,
         predicted_box,
         predicted_movement_time,
-        debug_zoom=True,
     ):
         camera_config = self.config.cameras[camera]
+        tom = self.tracked_object_metrics[camera]
+        zoom_factor = camera_config.onvif.autotracking.zoom_factor
+        max_target_box = calculate_max_target_box(zoom_factor)
 
         # frame width and height
         camera_width = camera_config.frame_shape[1]
@@ -1317,16 +1196,12 @@ class PtzAutoTracker:
         # absolute zooming separately from pan/tilt
         if camera_config.onvif.autotracking.zooming == ZoomingModeEnum.absolute:
             # don't zoom on initial move
-            if "target_box" not in self.tracked_object_metrics[camera]:
+            if "target_box" not in tom:
                 zoom = current_zoom_level
             else:
                 if (
                     result := self._should_zoom_in(
-                        camera,
+                        camera, obj.obj_data["box"], predicted_movement_time
-                        obj,
-                        obj.obj_data["box"],
-                        predicted_movement_time,
-                        debug_zoom,
                     )
                 ) is not None:
                     # divide zoom in 10 increments and always zoom out more than in
@@ -1342,46 +1217,35 @@ class PtzAutoTracker:
         # relative zooming concurrently with pan/tilt
         if camera_config.onvif.autotracking.zooming == ZoomingModeEnum.relative:
             # this is our initial zoom in on a new object
-            if "target_box" not in self.tracked_object_metrics[camera]:
+            if "target_box" not in tom:
-                zoom = target_box ** self.zoom_factor[camera]
+                zoom = target_box**zoom_factor
-                if zoom > self.tracked_object_metrics[camera]["max_target_box"]:
+                if zoom > max_target_box:
                     zoom = -(1 - zoom)
                 logger.debug(
-                    f"{camera}: target box: {target_box}, max: {self.tracked_object_metrics[camera]['max_target_box']}, calc zoom: {zoom}"
+                    f"{camera}: target box: {target_box}, max: {max_target_box}, calc zoom: {zoom}"
                 )
             else:
                 if (
                     result := self._should_zoom_in(
                         camera,
-                        obj,
                         predicted_box
                         if camera_config.onvif.autotracking.movement_weights
                         else obj.obj_data["box"],
                         predicted_movement_time,
-                        debug_zoom,
                     )
                 ) is not None:
-                    if predicted_movement_time:
+                    calculated_target_box = self._predict_target_box(
-                        calculated_target_box = self.tracked_object_metrics[camera][
-                            "target_box"
-                        ] + self._predict_area_after_time(
                         camera, predicted_movement_time
-                        ) / (camera_width * camera_height)
+                    )
+                    if predicted_movement_time:
                         logger.debug(
-                            f"{camera}: Zooming prediction: predicted movement time: {predicted_movement_time}, original box: {self.tracked_object_metrics[camera]['target_box']}, calculated box: {calculated_target_box}"
+                            f"{camera}: Zooming prediction: predicted movement time: {predicted_movement_time}, original box: {tom['target_box']}, calculated box: {calculated_target_box}"
                         )
-                    else:
-                        calculated_target_box = self.tracked_object_metrics[camera][
-                            "target_box"
-                        ]
                     # zoom value
-                    ratio = (
+                    ratio = max_target_box / calculated_target_box
-                        self.tracked_object_metrics[camera]["max_target_box"]
-                        / calculated_target_box
-                    )
                     zoom = (ratio - 1) / (ratio + 1)
                     logger.debug(
-                        f"{camera}: limit: {self.tracked_object_metrics[camera]['max_target_box']}, ratio: {ratio} zoom calculation: {zoom}"
+                        f"{camera}: limit: {max_target_box}, ratio: {ratio} zoom calculation: {zoom}"
                     )
                     if not result:
                         # zoom out with special condition if zooming out because of velocity, edges, etc.
@@ -1394,12 +1258,6 @@ class PtzAutoTracker:
 
         return zoom
 
-    def is_autotracking(self, camera: str):
-        return self.tracked_object[camera] is not None
-
-    def autotracked_object_region(self, camera: str):
-        return self.tracked_object[camera]["region"]
-
     def autotrack_object(self, camera: str, obj: TrackedObject):
         if camera not in self.config.cameras:
             return
@@ -1423,8 +1281,9 @@ class PtzAutoTracker:
                 # new object
                 self.tracked_object[camera] is None
                 and obj.camera_config.name == camera
-                and obj.obj_data["label"] in self.object_types[camera]
+                and obj.obj_data["label"] in camera_config.onvif.autotracking.track
-                and set(obj.entered_zones) & set(self.required_zones[camera])
+                and set(obj.entered_zones)
+                & set(camera_config.onvif.autotracking.required_zones)
                 and not obj.previous["false_positive"]
                 and not obj.false_positive
                 and not self.tracked_object_history[camera]
@@ -1433,7 +1292,6 @@ class PtzAutoTracker:
                 logger.debug(
                     f"{camera}: New object: {obj.obj_data['id']} {obj.obj_data['box']} {obj.obj_data['frame_time']}"
                 )
-                self.ptz_metrics[camera].tracking_active.set()
                 self.dispatcher.publish(
                     f"{camera}/ptz_autotracker/active", "ON", retain=False
                 )
@@ -1482,7 +1340,7 @@ class PtzAutoTracker:
                 # Should we check region (maybe too broad) or expand the previous object's box a bit and check that?
                 self.tracked_object[camera] is None
                 and obj.camera_config.name == camera
-                and obj.obj_data["label"] in self.object_types[camera]
+                and obj.obj_data["label"] in camera_config.onvif.autotracking.track
                 and not obj.previous["false_positive"]
                 and not obj.false_positive
                 and self.tracked_object_history[camera]
@@ -1518,10 +1376,7 @@ class PtzAutoTracker:
                     f"{camera}: End object: {obj.obj_data['id']} {obj.obj_data['box']}"
                 )
                 self.tracked_object[camera] = None
-                self.tracked_object_metrics[camera] = {
+                self._reset_tracked_object_metrics(camera)
-                    "max_target_box": AUTOTRACKING_MAX_AREA_RATIO
-                    ** (1 / self.zoom_factor[camera])
-                }
 
     async def camera_maintenance(self, camera):
         # bail and don't check anything if we're not set up yet, calibrating, or
@@ -1538,8 +1393,6 @@ class PtzAutoTracker:
         # returns camera to preset after timeout when tracking is over
         autotracker_config = self.config.cameras[camera].onvif.autotracking
 
-        if not self.autotracker_init[camera]:
-            self._autotracker_setup(self.config.cameras[camera], camera)
         # regularly update camera status
         if not self.ptz_metrics[camera].motor_stopped.is_set():
             await self.onvif.get_camera_status(camera)
@@ -1560,8 +1413,7 @@ class PtzAutoTracker:
             self.tracked_object[camera] = None
             self.tracked_object_history[camera].clear()
 
-            while not self.ptz_metrics[camera].motor_stopped.is_set():
+            await self._wait_until_stopped(camera)
-                await self.onvif.get_camera_status(camera)
             logger.debug(
                 f"{camera}: Time is {self.ptz_metrics[camera].frame_time.value}, returning to preset: {autotracker_config.return_preset}"
             )
@@ -1571,10 +1423,8 @@ class PtzAutoTracker:
             )
 
             # update stored zoom level from preset
-            while not self.ptz_metrics[camera].motor_stopped.is_set():
+            await self._wait_until_stopped(camera)
-                await self.onvif.get_camera_status(camera)
 
-            self.ptz_metrics[camera].tracking_active.clear()
             self.dispatcher.publish(
                 f"{camera}/ptz_autotracker/active", "OFF", retain=False
             )

frigate/ptz/onvif.py

@@ -10,8 +10,7 @@ from pathlib import Path
 from typing import Any
 
 import numpy
-from onvif import ONVIFCamera, ONVIFError, ONVIFService
+from onvif import ONVIFCamera, ONVIFService
-from zeep.exceptions import Fault, TransportError
 
 from frigate.camera import PTZMetrics
 from frigate.config import FrigateConfig, ZoomingModeEnum
@@ -41,6 +40,14 @@ class OnvifCommandEnum(str, Enum):
     focus_out = "focus_out"
 
 
+PAN_TILT_VELOCITY = {
+    OnvifCommandEnum.move_left: (-0.5, 0),
+    OnvifCommandEnum.move_right: (0.5, 0),
+    OnvifCommandEnum.move_up: (0, 0.5),
+    OnvifCommandEnum.move_down: (0, -0.5),
+}
+
+
 class OnvifController:
     ptz_metrics: dict[str, PTZMetrics]
 
@@ -61,14 +68,6 @@ class OnvifController:
         self.loop_thread = threading.Thread(target=self._run_event_loop, daemon=True)
         self.loop_thread.start()
 
-        self.camera_configs = {}
-        for cam_name, cam in config.cameras.items():
-            if not cam.enabled:
-                continue
-            if cam.onvif.host:
-                self.camera_configs[cam_name] = cam
-                self.status_locks[cam_name] = asyncio.Lock()
-
         self.config_subscriber = CameraConfigUpdateSubscriber(
             self.config,
             self.config.cameras,
@@ -92,7 +91,8 @@ class OnvifController:
 
     async def _init_cameras(self) -> None:
         """Initialize all configured cameras."""
-        for cam_name in self.camera_configs:
+        for cam_name, cam in list(self.config.cameras.items()):
+            if cam.enabled and cam.onvif.host:
                 await self._init_single_camera(cam_name)
 
     async def _poll_config_updates(self) -> None:
@@ -129,13 +129,9 @@ class OnvifController:
 
     async def _remove_camera(self, cam_name: str) -> None:
         """Tear down the ONVIF session for a camera removed at runtime."""
-        if cam_name not in self.cams and cam_name not in self.camera_configs:
-            return
-
         logger.debug(f"Tearing down ONVIF for {cam_name} after camera removal")
         await self._close_camera(cam_name)
         self.cams.pop(cam_name, None)
-        self.camera_configs.pop(cam_name, None)
         self.failed_cams.pop(cam_name, None)
         self.status_locks.pop(cam_name, None)
 
@@ -143,24 +139,13 @@ class OnvifController:
         """Re-initialize a camera after config change."""
         logger.info(f"Re-initializing ONVIF for {cam_name} due to config change")
 
-        # close existing session before re-init
+        # close existing session and reset state before re-init
         await self._close_camera(cam_name)
+        self.cams.pop(cam_name, None)
+        self.failed_cams.pop(cam_name, None)
 
         cam = self.config.cameras.get(cam_name)
-        if not cam or not cam.onvif.host:
+        if cam and cam.onvif.host:
-            # ONVIF removed from config, clean up
-            self.cams.pop(cam_name, None)
-            self.camera_configs.pop(cam_name, None)
-            self.failed_cams.pop(cam_name, None)
-            return
-
-        # update stored config and reset state
-        self.camera_configs[cam_name] = cam
-        if cam_name not in self.status_locks:
-            self.status_locks[cam_name] = asyncio.Lock()
-        self.cams.pop(cam_name, None)
-        self.failed_cams.pop(cam_name, None)
-
             await self._init_single_camera(cam_name)
 
     async def _init_single_camera(self, cam_name: str) -> bool:
@@ -172,11 +157,12 @@ class OnvifController:
         Returns:
             bool: True if initialization succeeded, False otherwise
         """
-        if cam_name not in self.camera_configs:
+        cam = self.config.cameras.get(cam_name)
+        if cam is None:
             logger.error(f"No configuration found for camera {cam_name}")
             return False
 
-        cam = self.camera_configs[cam_name]
+        self.status_locks.setdefault(cam_name, asyncio.Lock())
         try:
             self.cams[cam_name] = {
                 "onvif": ONVIFCamera(
@@ -195,12 +181,11 @@ class OnvifController:
                 "profiles": [],
             }
             return True
-        except (Fault, ONVIFError, TransportError, Exception) as e:
+        except Exception as e:
             logger.error(f"Failed to create ONVIF camera instance for {cam_name}: {e}")
             # track initial failures
             self.failed_cams[cam_name] = {
                 "retry_attempts": 0,
-                "last_error": str(e),
                 "last_attempt": time.time(),
             }
             return False
@@ -211,7 +196,8 @@ class OnvifController:
         if camera_config is None:
             return False
 
-        onvif: ONVIFCamera = self.cams[camera_name]["onvif"]
+        cam = self.cams[camera_name]
+        onvif: ONVIFCamera = cam["onvif"]
         try:
             await onvif.update_xaddrs()
         except Exception as e:
@@ -226,7 +212,7 @@ class OnvifController:
             # this will fire an exception if camera is not a ptz
             capabilities = onvif.get_definition("ptz")
             logger.debug(f"Onvif capabilities for {camera_name}: {capabilities}")
-        except (Fault, ONVIFError, TransportError, Exception) as e:
+        except Exception as e:
             logger.error(
                 f"Unable to get Onvif capabilities for camera: {camera_name}: {e}"
             )
@@ -235,7 +221,7 @@ class OnvifController:
         try:
             profiles = await media.GetProfiles()
             logger.debug(f"Onvif profiles for {camera_name}: {profiles}")
-        except (Fault, ONVIFError, TransportError, Exception) as e:
+        except Exception as e:
             logger.error(
                 f"Unable to get Onvif media profiles for camera: {camera_name}: {e}"
             )
@@ -254,7 +240,7 @@ class OnvifController:
         ]
 
         # store available profiles for API response and log for debugging
-        self.cams[camera_name]["profiles"] = [
+        cam["profiles"] = [
             {"name": getattr(p, "Name", None) or p.token, "token": p.token}
             for p in valid_profiles
         ]
@@ -267,19 +253,18 @@ class OnvifController:
             )
 
         configured_profile = camera_config.onvif.profile
-        profile = None
 
         if configured_profile is not None:
             # match by exact token first, then by name
-            for p in valid_profiles:
+            profile = next(
-                if p.token == configured_profile:
+                (
-                    profile = p
+                    p
-                    break
+                    for key in ("token", "Name")
-            if profile is None:
+                    for p in valid_profiles
-                for p in valid_profiles:
+                    if getattr(p, key, None) == configured_profile
-                    if getattr(p, "Name", None) == configured_profile:
+                ),
-                        profile = p
+                None,
-                        break
+            )
             if profile is None:
                 available = [
                     f"name='{getattr(p, 'Name', None)}', token='{p.token}'"
@@ -304,39 +289,30 @@ class OnvifController:
 
         logger.debug(f"Selected Onvif profile for {camera_name}: {profile}")
 
-        # get the PTZ config for the profile
-        try:
         configs = profile.PTZConfiguration
-            logger.debug(
+        logger.debug(f"Onvif ptz config for media profile in {camera_name}: {configs}")
-                f"Onvif ptz config for media profile in {camera_name}: {configs}"
-            )
-        except Exception as e:
-            logger.error(
-                f"Invalid Onvif PTZ configuration for camera: {camera_name}: {e}"
-            )
-            return False
 
         ptz: ONVIFService = await onvif.create_ptz_service()
-        self.cams[camera_name]["ptz"] = ptz
+        cam["ptz"] = ptz
 
         try:
             imaging: ONVIFService = await onvif.create_imaging_service()
-        except (Fault, ONVIFError, TransportError, Exception) as e:
+        except Exception as e:
             logger.debug(f"Imaging service not supported for {camera_name}: {e}")
             imaging = None
-        self.cams[camera_name]["imaging"] = imaging
+        cam["imaging"] = imaging
         try:
             video_sources = await media.GetVideoSources()
             if video_sources and len(video_sources) > 0:
-                self.cams[camera_name]["video_source_token"] = video_sources[0].token
+                cam["video_source_token"] = video_sources[0].token
-        except (Fault, ONVIFError, TransportError, Exception) as e:
+        except Exception as e:
             logger.debug(f"Unable to get video sources for {camera_name}: {e}")
-            self.cams[camera_name]["video_source_token"] = None
+            cam["video_source_token"] = None
 
         # setup continuous moving request
         move_request = ptz.create_type("ContinuousMove")
         move_request.ProfileToken = profile.token
-        self.cams[camera_name]["move_request"] = move_request
+        cam["move_request"] = move_request
 
         # get PTZ configuration options for feature detection and relative movement
         ptz_config = None
@@ -349,7 +325,7 @@ class OnvifController:
             logger.debug(
                 f"Onvif PTZ configuration options for {camera_name}: {ptz_config}"
             )
-        except (Fault, ONVIFError, TransportError, Exception) as e:
+        except Exception as e:
             logger.debug(
                 f"Unable to get PTZ configuration options for {camera_name}: {e}"
             )
@@ -375,18 +351,6 @@ class OnvifController:
             autotracking_config.enabled_in_config and autotracking_config.enabled
         )
 
-        # these are local and cost nothing to build, and autotracking can be enabled
-        # after a camera is initialized, so always create them rather than baking the
-        # current config value into init state
-        status_request = ptz.create_type("GetStatus")
-        status_request.ProfileToken = profile.token
-        self.cams[camera_name]["status_request"] = status_request
-
-        service_capabilities_request = ptz.create_type("GetServiceCapabilities")
-        self.cams[camera_name]["service_capabilities_request"] = (
-            service_capabilities_request
-        )
-
         # setup relative move request when FOV relative movement is supported
         if (
             fov_space_id is not None
@@ -395,9 +359,7 @@ class OnvifController:
             # one-off GetStatus to seed Translation field
             status = None
             try:
-                one_off_status_request = ptz.create_type("GetStatus")
+                status = await ptz.GetStatus({"ProfileToken": profile.token})
-                one_off_status_request.ProfileToken = profile.token
-                status = await ptz.GetStatus(one_off_status_request)
                 logger.debug(f"Onvif status for {camera_name}: {status}")
             except Exception as e:
                 logger.warning(f"Unable to get status from camera {camera_name}: {e}")
@@ -424,7 +386,7 @@ class OnvifController:
             # configure zoom on relative move request
             if (
                 autotracking_enabled
-                and autotracking_config.zooming != ZoomingModeEnum.disabled
+                and autotracking_config.zooming == ZoomingModeEnum.relative
             ):
                 zoom_space_id = next(
                     (
@@ -463,21 +425,16 @@ class OnvifController:
                 )
 
             if rel_move_request.Speed is None:
-                rel_move_request.Speed = configs.DefaultPTZSpeed if configs else None
+                rel_move_request.Speed = configs.DefaultPTZSpeed
             logger.debug(
                 f"{camera_name}: Relative move request after setup: {rel_move_request}"
             )
-            self.cams[camera_name]["relative_move_request"] = rel_move_request
+            cam["relative_move_request"] = rel_move_request
-
-        # setup absolute move request
-        abs_move_request = ptz.create_type("AbsoluteMove")
-        abs_move_request.ProfileToken = profile.token
-        self.cams[camera_name]["absolute_move_request"] = abs_move_request
 
         # setup existing presets
         try:
             presets: list[dict] = await ptz.GetPresets({"ProfileToken": profile.token})
-        except (Fault, ONVIFError, TransportError, Exception) as e:
+        except Exception as e:
             logger.warning(f"Unable to get presets from camera: {camera_name}: {e}")
             presets = []
 
@@ -490,7 +447,7 @@ class OnvifController:
                 preset_name = preset_name.encode("latin-1").decode("utf-8")
             except (UnicodeEncodeError, UnicodeDecodeError):
                 pass
-            self.cams[camera_name]["presets"][preset_name.lower()] = preset["token"]
+            cam["presets"][preset_name.lower()] = preset["token"]
 
         # get list of supported features
         supported_features = []
@@ -504,64 +461,40 @@ class OnvifController:
         if configs.DefaultRelativePanTiltTranslationSpace:
             supported_features.append("pt-r")
 
+        spaces = getattr(ptz_config, "Spaces", None)
+
         if configs.DefaultRelativeZoomTranslationSpace:
             supported_features.append("zoom-r")
-            if ptz_config is not None:
+            if getattr(spaces, "RelativeZoomTranslationSpace", None):
-                try:
+                cam["relative_zoom_range"] = spaces.RelativeZoomTranslationSpace[0]
-                    self.cams[camera_name]["relative_zoom_range"] = (
-                        ptz_config.Spaces.RelativeZoomTranslationSpace[0]
-                    )
-                except Exception as e:
-                    if autotracking_config.zooming == ZoomingModeEnum.relative:
-                        autotracking_config.zooming = ZoomingModeEnum.disabled
-                        logger.warning(
-                            f"Disabling autotracking zooming for {camera_name}: Relative zoom not supported. Exception: {e}"
-                        )
 
         if configs.DefaultAbsoluteZoomPositionSpace:
             supported_features.append("zoom-a")
-            if ptz_config is not None:
+            if getattr(spaces, "AbsoluteZoomPositionSpace", None):
-                try:
+                cam["absolute_zoom_range"] = spaces.AbsoluteZoomPositionSpace[0]
-                    self.cams[camera_name]["absolute_zoom_range"] = (
-                        ptz_config.Spaces.AbsoluteZoomPositionSpace[0]
-                    )
-                    self.cams[camera_name]["zoom_limits"] = configs.ZoomLimits
-                except Exception as e:
-                    if autotracking_config.zooming != ZoomingModeEnum.disabled:
-                        autotracking_config.zooming = ZoomingModeEnum.disabled
-                        logger.warning(
-                            f"Disabling autotracking zooming for {camera_name}: Absolute zoom not supported. Exception: {e}"
-                        )
 
-        # disable autotracking zoom if required ranges are unavailable
+        # autotracking zoom needs the range for its mode, and get_camera_status
-        if autotracking_config.zooming != ZoomingModeEnum.disabled:
+        # reads the absolute range in both modes
-            if autotracking_config.zooming == ZoomingModeEnum.relative:
+        zooming = autotracking_config.zooming
-                if "relative_zoom_range" not in self.cams[camera_name]:
+        if zooming != ZoomingModeEnum.disabled and (
-                    autotracking_config.zooming = ZoomingModeEnum.disabled
+            "absolute_zoom_range" not in cam or f"{zooming.value}_zoom_range" not in cam
-                    logger.warning(
-                        f"Disabling autotracking zooming for {camera_name}: Relative zoom range unavailable"
-                    )
-            if autotracking_config.zooming == ZoomingModeEnum.absolute:
-                if "absolute_zoom_range" not in self.cams[camera_name]:
-                    autotracking_config.zooming = ZoomingModeEnum.disabled
-                    logger.warning(
-                        f"Disabling autotracking zooming for {camera_name}: Absolute zoom range unavailable"
-                    )
-
-        if (
-            self.cams[camera_name]["video_source_token"] is not None
-            and imaging is not None
         ):
+            autotracking_config.zooming = ZoomingModeEnum.disabled
+            logger.warning(
+                f"Disabling autotracking zooming for {camera_name}: {zooming.value} zoom range unavailable"
+            )
+
+        if cam["video_source_token"] is not None and imaging is not None:
             try:
                 imaging_capabilities = await imaging.GetImagingSettings(
-                    {"VideoSourceToken": self.cams[camera_name]["video_source_token"]}
+                    {"VideoSourceToken": cam["video_source_token"]}
                 )
                 if (
                     hasattr(imaging_capabilities, "Focus")
                     and imaging_capabilities.Focus
                 ):
                     supported_features.append("focus")
-            except (Fault, ONVIFError, TransportError, Exception) as e:
+            except Exception as e:
                 logger.debug(f"Focus not supported for {camera_name}: {e}")
 
         # detect FOV relative movement support
@@ -570,17 +503,18 @@ class OnvifController:
             and configs.DefaultRelativePanTiltTranslationSpace is not None
         ):
             supported_features.append("pt-r-fov")
-            self.cams[camera_name]["relative_fov_range"] = (
+            cam["relative_fov_range"] = (
                 ptz_config.Spaces.RelativePanTiltTranslationSpace[fov_space_id]
             )
 
-        self.cams[camera_name]["features"] = supported_features
+        cam["features"] = supported_features
-        self.cams[camera_name]["init"] = True
+        cam["init"] = True
         return True
 
     async def _stop(self, camera_name: str) -> None:
-        move_request = self.cams[camera_name]["move_request"]
+        cam = self.cams[camera_name]
-        await self.cams[camera_name]["ptz"].Stop(
+        move_request = cam["move_request"]
+        await cam["ptz"].Stop(
             {
                 "ProfileToken": move_request.ProfileToken,
                 "PanTilt": True,
@@ -588,88 +522,75 @@ class OnvifController:
             }
         )
         if (
-            "focus" in self.cams[camera_name]["features"]
+            "focus" in cam["features"]
-            and self.cams[camera_name]["video_source_token"]
+            and cam["video_source_token"]
-            and self.cams[camera_name]["imaging"] is not None
+            and cam["imaging"] is not None
         ):
             try:
-                stop_request = self.cams[camera_name]["imaging"].create_type("Stop")
+                stop_request = cam["imaging"].create_type("Stop")
-                stop_request.VideoSourceToken = self.cams[camera_name][
+                stop_request.VideoSourceToken = cam["video_source_token"]
-                    "video_source_token"
+                await cam["imaging"].Stop(stop_request)
-                ]
+            except Exception as e:
-                await self.cams[camera_name]["imaging"].Stop(stop_request)
-            except (Fault, ONVIFError, TransportError, Exception) as e:
                 logger.warning(f"Failed to stop focus for {camera_name}: {e}")
-        self.cams[camera_name]["active"] = False
+        cam["active"] = False
 
     async def _move(self, camera_name: str, command: OnvifCommandEnum) -> None:
-        if self.cams[camera_name]["active"]:
+        cam = self.cams[camera_name]
+
+        if cam["active"]:
             logger.warning(
                 f"{camera_name} is already performing an action, stopping..."
             )
             await self._stop(camera_name)
 
-        if "pt" not in self.cams[camera_name]["features"]:
+        if "pt" not in cam["features"]:
             logger.error(f"{camera_name} does not support ONVIF pan/tilt movement.")
             return
 
-        self.cams[camera_name]["active"] = True
+        cam["active"] = True
-        move_request = self.cams[camera_name]["move_request"]
+        move_request = cam["move_request"]
 
-        if command == OnvifCommandEnum.move_left:
+        x, y = PAN_TILT_VELOCITY[command]
-            move_request.Velocity = {"PanTilt": {"x": -0.5, "y": 0}}
+        move_request.Velocity = {"PanTilt": {"x": x, "y": y}}
-        elif command == OnvifCommandEnum.move_right:
-            move_request.Velocity = {"PanTilt": {"x": 0.5, "y": 0}}
-        elif command == OnvifCommandEnum.move_up:
-            move_request.Velocity = {
-                "PanTilt": {
-                    "x": 0,
-                    "y": 0.5,
-                }
-            }
-        elif command == OnvifCommandEnum.move_down:
-            move_request.Velocity = {
-                "PanTilt": {
-                    "x": 0,
-                    "y": -0.5,
-                }
-            }
 
         try:
-            await self.cams[camera_name]["ptz"].ContinuousMove(move_request)
+            await cam["ptz"].ContinuousMove(move_request)
-        except (Fault, ONVIFError, TransportError, Exception) as e:
+        except Exception as e:
             logger.warning(f"Onvif sending move request to {camera_name} failed: {e}")
 
     async def _move_relative(self, camera_name: str, pan, tilt, zoom, speed) -> None:
-        if "pt-r-fov" not in self.cams[camera_name]["features"]:
+        cam = self.cams[camera_name]
+
+        if "pt-r-fov" not in cam["features"]:
             logger.error(f"{camera_name} does not support ONVIF RelativeMove (FOV).")
             return
 
         metrics = self.ptz_metrics.get(camera_name)
+        camera_config = self.config.cameras.get(camera_name)
 
-        if metrics is None:
+        if metrics is None or camera_config is None:
             return
 
         logger.debug(
             f"{camera_name} called RelativeMove: pan: {pan} tilt: {tilt} zoom: {zoom}"
         )
 
-        if self.cams[camera_name]["active"]:
+        if cam["active"]:
             logger.warning(
                 f"{camera_name} is already performing an action, not moving..."
             )
             return
 
-        self.cams[camera_name]["active"] = True
+        cam["active"] = True
 
         # only track start_time for autotracking
-        if metrics.autotracker_enabled.value:
+        if camera_config.onvif.autotracking.enabled:
             metrics.motor_stopped.clear()
             logger.debug(f"{camera_name}: PTZ start time: {metrics.frame_time.value}")
             metrics.start_time.value = metrics.frame_time.value
             metrics.stop_time.value = 0
 
-        move_request = self.cams[camera_name]["relative_move_request"]
+        move_request = cam["relative_move_request"]
 
         # function takes in -1 to 1 for pan and tilt, interpolate to the values of the camera.
         # The onvif spec says this can report as +INF and -INF, so this may need to be modified
@@ -677,55 +598,49 @@ class OnvifController:
             pan,
             [-1, 1],
             [
-                self.cams[camera_name]["relative_fov_range"]["XRange"]["Min"],
+                cam["relative_fov_range"]["XRange"]["Min"],
-                self.cams[camera_name]["relative_fov_range"]["XRange"]["Max"],
+                cam["relative_fov_range"]["XRange"]["Max"],
             ],
         )
         tilt = numpy.interp(
             tilt,
             [-1, 1],
             [
-                self.cams[camera_name]["relative_fov_range"]["YRange"]["Min"],
+                cam["relative_fov_range"]["YRange"]["Min"],
-                self.cams[camera_name]["relative_fov_range"]["YRange"]["Max"],
+                cam["relative_fov_range"]["YRange"]["Max"],
             ],
         )
 
-        move_request.Speed = {
+        move_speed = {"PanTilt": {"x": speed, "y": speed}}
-            "PanTilt": {
-                "x": speed,
-                "y": speed,
-            },
-        }
 
         move_request.Translation.PanTilt.x = pan
         move_request.Translation.PanTilt.y = tilt
 
         # include zoom if requested and camera supports relative zoom
-        if zoom != 0 and "zoom-r" in self.cams[camera_name]["features"]:
+        include_zoom = zoom != 0 and "zoom-r" in cam["features"]
-            move_request.Speed = {
+
-                "PanTilt": {
+        if include_zoom:
-                    "x": speed,
+            move_speed["Zoom"] = {"x": speed}
-                    "y": speed,
-                },
-                "Zoom": {"x": speed},
-            }
             move_request["Translation"]["Zoom"] = {"x": zoom}
 
-        await self.cams[camera_name]["ptz"].RelativeMove(move_request)
+        move_request.Speed = move_speed
+
+        await cam["ptz"].RelativeMove(move_request)
 
         # reset after the move request
         move_request.Translation.PanTilt.x = 0
         move_request.Translation.PanTilt.y = 0
 
-        if zoom != 0 and "zoom-r" in self.cams[camera_name]["features"]:
+        if include_zoom:
             del move_request["Translation"]["Zoom"]
 
-        self.cams[camera_name]["active"] = False
+        cam["active"] = False
 
     async def _move_to_preset(self, camera_name: str, preset: str) -> None:
+        cam = self.cams[camera_name]
         preset = preset.lower()
 
-        if preset not in self.cams[camera_name]["presets"]:
+        if preset not in cam["presets"]:
             logger.error(f"{preset} is not a valid preset for {camera_name}")
             return
 
@@ -734,44 +649,48 @@ class OnvifController:
         if metrics is None:
             return
 
-        self.cams[camera_name]["active"] = True
+        cam["active"] = True
         metrics.start_time.value = 0
         metrics.stop_time.value = 0
-        move_request = self.cams[camera_name]["move_request"]
+        move_request = cam["move_request"]
-        preset_token = self.cams[camera_name]["presets"][preset]
+        preset_token = cam["presets"][preset]
 
-        await self.cams[camera_name]["ptz"].GotoPreset(
+        await cam["ptz"].GotoPreset(
             {
                 "ProfileToken": move_request.ProfileToken,
                 "PresetToken": preset_token,
             }
         )
 
-        self.cams[camera_name]["active"] = False
+        cam["active"] = False
 
     async def _zoom(self, camera_name: str, command: OnvifCommandEnum) -> None:
-        if self.cams[camera_name]["active"]:
+        cam = self.cams[camera_name]
+
+        if cam["active"]:
             logger.warning(
                 f"{camera_name} is already performing an action, stopping..."
             )
             await self._stop(camera_name)
 
-        if "zoom" not in self.cams[camera_name]["features"]:
+        if "zoom" not in cam["features"]:
             logger.error(f"{camera_name} does not support ONVIF zooming.")
             return
 
-        self.cams[camera_name]["active"] = True
+        cam["active"] = True
-        move_request = self.cams[camera_name]["move_request"]
+        move_request = cam["move_request"]
 
         if command == OnvifCommandEnum.zoom_in:
             move_request.Velocity = {"Zoom": {"x": 0.5}}
         elif command == OnvifCommandEnum.zoom_out:
             move_request.Velocity = {"Zoom": {"x": -0.5}}
 
-        await self.cams[camera_name]["ptz"].ContinuousMove(move_request)
+        await cam["ptz"].ContinuousMove(move_request)
 
     async def _zoom_absolute(self, camera_name: str, zoom, speed) -> None:
-        if "zoom-a" not in self.cams[camera_name]["features"]:
+        cam = self.cams[camera_name]
+
+        if "zoom-a" not in cam["features"]:
             logger.error(f"{camera_name} does not support ONVIF AbsoluteMove zooming.")
             return
 
@@ -782,56 +701,59 @@ class OnvifController:
 
         logger.debug(f"{camera_name} called AbsoluteMove: zoom: {zoom}")
 
-        if self.cams[camera_name]["active"]:
+        if cam["active"]:
             logger.warning(
                 f"{camera_name} is already performing an action, not moving..."
             )
             return
 
-        self.cams[camera_name]["active"] = True
+        cam["active"] = True
         metrics.motor_stopped.clear()
         logger.debug(f"{camera_name}: PTZ start time: {metrics.frame_time.value}")
         metrics.start_time.value = metrics.frame_time.value
         metrics.stop_time.value = 0
-        move_request = self.cams[camera_name]["absolute_move_request"]
-
         # function takes in 0 to 1 for zoom, interpolate to the values of the camera.
         zoom = numpy.interp(
             zoom,
             [0, 1],
             [
-                self.cams[camera_name]["absolute_zoom_range"]["XRange"]["Min"],
+                cam["absolute_zoom_range"]["XRange"]["Min"],
-                self.cams[camera_name]["absolute_zoom_range"]["XRange"]["Max"],
+                cam["absolute_zoom_range"]["XRange"]["Max"],
             ],
         )
 
-        move_request.Speed = {"Zoom": speed}
-        move_request.Position = {"Zoom": zoom}
-
         logger.debug(f"{camera_name}: Absolute zoom: {zoom}")
 
-        await self.cams[camera_name]["ptz"].AbsoluteMove(move_request)
+        await cam["ptz"].AbsoluteMove(
+            {
+                "ProfileToken": cam["move_request"].ProfileToken,
+                "Position": {"Zoom": zoom},
+                "Speed": {"Zoom": speed},
+            }
+        )
 
-        self.cams[camera_name]["active"] = False
+        cam["active"] = False
 
     async def _focus(self, camera_name: str, command: OnvifCommandEnum) -> None:
-        if self.cams[camera_name]["active"]:
+        cam = self.cams[camera_name]
+
+        if cam["active"]:
             logger.warning(
                 f"{camera_name} is already performing an action, not moving..."
             )
             await self._stop(camera_name)
 
         if (
-            "focus" not in self.cams[camera_name]["features"]
+            "focus" not in cam["features"]
-            or not self.cams[camera_name]["video_source_token"]
+            or not cam["video_source_token"]
-            or self.cams[camera_name]["imaging"] is None
+            or cam["imaging"] is None
         ):
             logger.error(f"{camera_name} does not support ONVIF continuous focus.")
             return
 
-        self.cams[camera_name]["active"] = True
+        cam["active"] = True
-        move_request = self.cams[camera_name]["imaging"].create_type("Move")
+        move_request = cam["imaging"].create_type("Move")
-        move_request.VideoSourceToken = self.cams[camera_name]["video_source_token"]
+        move_request.VideoSourceToken = cam["video_source_token"]
         move_request.Focus = {
             "Continuous": {
                 "Speed": 0.5 if command == OnvifCommandEnum.focus_in else -0.5
@@ -839,10 +761,10 @@ class OnvifController:
         }
 
         try:
-            await self.cams[camera_name]["imaging"].Move(move_request)
+            await cam["imaging"].Move(move_request)
-        except (Fault, ONVIFError, TransportError, Exception) as e:
+        except Exception as e:
             logger.warning(f"Onvif sending focus request to {camera_name} failed: {e}")
-            self.cams[camera_name]["active"] = False
+            cam["active"] = False
 
     async def handle_command_async(
         self, camera_name: str, command: OnvifCommandEnum, param: str = ""
@@ -883,7 +805,7 @@ class OnvifController:
                 await self._focus(camera_name, command)
             else:
                 await self._move(camera_name, command)
-        except (Fault, ONVIFError, TransportError, Exception) as e:
+        except Exception as e:
             logger.error(f"Unable to handle onvif command: {e}")
 
     def handle_command(
@@ -906,6 +828,15 @@ class OnvifController:
                 f"Error executing command {command} for camera {camera_name}: {e}"
             )
 
+    def _camera_info(self, camera_name: str) -> dict[str, Any]:
+        cam = self.cams[camera_name]
+        return {
+            "name": camera_name,
+            "features": cam["features"],
+            "presets": list(cam["presets"]),
+            "profiles": cam["profiles"],
+        }
+
     async def get_camera_info(self, camera_name: str) -> dict[str, Any]:
         """
         Get ptz capabilities and presets, attempting to reconnect if ONVIF is configured
@@ -929,56 +860,21 @@ class OnvifController:
             return {}
 
         if camera_name in self.cams.keys() and self.cams[camera_name]["init"]:
-            return {
+            return self._camera_info(camera_name)
-                "name": camera_name,
-                "features": self.cams[camera_name]["features"],
-                "presets": list(self.cams[camera_name]["presets"].keys()),
-                "profiles": self.cams[camera_name].get("profiles", []),
-            }
 
         if camera_name not in self.cams.keys() and camera_name in self.config.cameras:
             success = await self._init_single_camera(camera_name)
             if not success:
                 return {}
 
-        # Reset retry count after timeout
+        failed = self.failed_cams.get(camera_name, {})
-        attempts = self.failed_cams.get(camera_name, {}).get("retry_attempts", 0)
+        attempts = failed.get("retry_attempts", 0)
-        last_attempt = self.failed_cams.get(camera_name, {}).get("last_attempt", 0)
+        last_attempt = failed.get("last_attempt", 0)
 
+        # Reset retry count after timeout
         if last_attempt and (time.time() - last_attempt) > self.reset_timeout:
             logger.debug(f"Resetting retry count for {camera_name} after timeout")
             attempts = 0
-            self.failed_cams[camera_name]["retry_attempts"] = 0
-
-        # Attempt initialization/reconnection
-        if attempts < self.max_retries:
-            logger.info(
-                f"Attempting ONVIF initialization for {camera_name} (retry {attempts + 1}/{self.max_retries})"
-            )
-            try:
-                if await self._init_onvif(camera_name):
-                    if camera_name in self.failed_cams:
-                        del self.failed_cams[camera_name]
-                    return {
-                        "name": camera_name,
-                        "features": self.cams[camera_name]["features"],
-                        "presets": list(self.cams[camera_name]["presets"].keys()),
-                    }
-                else:
-                    logger.warning(f"ONVIF initialization failed for {camera_name}")
-            except Exception as e:
-                logger.error(
-                    f"Error during ONVIF initialization for {camera_name}: {e}"
-                )
-                if camera_name not in self.failed_cams:
-                    self.failed_cams[camera_name] = {"retry_attempts": 0}
-                self.failed_cams[camera_name].update(
-                    {
-                        "retry_attempts": attempts + 1,
-                        "last_error": str(e),
-                        "last_attempt": time.time(),
-                    }
-                )
 
         if attempts >= self.max_retries:
             remaining_time = max(
@@ -987,8 +883,24 @@ class OnvifController:
             logger.error(
                 f"Too many ONVIF initialization attempts for {camera_name}, retry in {remaining_time} minute{'s' if remaining_time != 1 else ''}"
             )
+            return {}
 
-        logger.debug(f"Could not initialize ONVIF for {camera_name}")
+        logger.info(
+            f"Attempting ONVIF initialization for {camera_name} (retry {attempts + 1}/{self.max_retries})"
+        )
+        try:
+            if await self._init_onvif(camera_name):
+                self.failed_cams.pop(camera_name, None)
+                return self._camera_info(camera_name)
+
+            logger.warning(f"ONVIF initialization failed for {camera_name}")
+        except Exception as e:
+            logger.error(f"Error during ONVIF initialization for {camera_name}: {e}")
+
+        self.failed_cams[camera_name] = {
+            "retry_attempts": attempts + 1,
+            "last_attempt": time.time(),
+        }
         return {}
 
     async def get_service_capabilities(self, camera_name: str) -> None:
@@ -996,16 +908,13 @@ class OnvifController:
             logger.error(f"ONVIF is not configured for {camera_name}")
             return {}
 
-        if not self.cams[camera_name]["init"]:
+        cam = self.cams[camera_name]
+
+        if not cam["init"]:
             await self._init_onvif(camera_name)
 
-        service_capabilities_request = self.cams[camera_name][
-            "service_capabilities_request"
-        ]
         try:
-            service_capabilities = await self.cams[camera_name][
+            service_capabilities = await cam["ptz"].GetServiceCapabilities()
-                "ptz"
-            ].GetServiceCapabilities(service_capabilities_request)
 
             logger.debug(
                 f"Onvif service capabilities for {camera_name}: {service_capabilities}"
@@ -1031,13 +940,16 @@ class OnvifController:
             if metrics is None or camera_config is None:
                 return
 
-            if not self.cams[camera_name]["init"]:
+            cam = self.cams[camera_name]
+
+            if not cam["init"]:
                 if not await self._init_onvif(camera_name):
                     return
 
-            status_request = self.cams[camera_name]["status_request"]
             try:
-                status = await self.cams[camera_name]["ptz"].GetStatus(status_request)
+                status = await cam["ptz"].GetStatus(
+                    {"ProfileToken": cam["move_request"].ProfileToken}
+                )
             except Exception:
                 pass  # We're unsupported, that'll be reported in the next check.
 
@@ -1068,7 +980,7 @@ class OnvifController:
             if pan_tilt_status == "IDLE" and (
                 zoom_status is None or zoom_status == "IDLE"
             ):
-                self.cams[camera_name]["active"] = False
+                cam["active"] = False
                 if not metrics.motor_stopped.is_set():
                     metrics.motor_stopped.set()
 
@@ -1078,7 +990,7 @@ class OnvifController:
 
                     metrics.stop_time.value = metrics.frame_time.value
             else:
-                self.cams[camera_name]["active"] = True
+                cam["active"] = True
                 if metrics.motor_stopped.is_set():
                     metrics.motor_stopped.clear()
 
@@ -1094,8 +1006,8 @@ class OnvifController:
                 metrics.zoom_level.value = numpy.interp(
                     round(status.Position.Zoom.x, 2),
                     [
-                        self.cams[camera_name]["absolute_zoom_range"]["XRange"]["Min"],
+                        cam["absolute_zoom_range"]["XRange"]["Min"],
-                        self.cams[camera_name]["absolute_zoom_range"]["XRange"]["Max"],
+                        cam["absolute_zoom_range"]["XRange"]["Max"],
                     ],
                     [0, 1],
                 )
@@ -1134,7 +1046,7 @@ class OnvifController:
 
     def close(self) -> None:
         """Gracefully shut down the ONVIF controller."""
-        if not hasattr(self, "loop") or self.loop.is_closed():
+        if self.loop.is_closed():
             logger.debug("ONVIF controller already closed")
             return
 
@@ -1151,13 +1063,6 @@ class OnvifController:
 
         self.config_subscriber.stop()
 
-        def stop_and_cleanup():
+        self.loop.call_soon_threadsafe(self.loop.stop)
-            try:
-                self.loop.stop()
-            except Exception as e:
-                logger.error(f"Error during loop cleanup: {e}")
-
-        # Schedule stop and cleanup in the loop thread
-        self.loop.call_soon_threadsafe(stop_and_cleanup)
 
         self.loop_thread.join()

frigate/record/export.py

@@ -42,6 +42,8 @@ from frigate.util.ownership import chown_to_runtime
 from frigate.util.recording_coverage import (
     build_spans,
     known_video_codecs,
+    null_audio_glitches,
+    realized_timeline,
     resolve_coverage,
     stream_media_summary,
 )
@@ -84,10 +86,20 @@ class StreamRun:
 
 @dataclass
 class _ChapterWindow:
-    """A merged-timeline slice, shaped like the recording rows chapters read."""
+    """A merged-timeline slice, shaped like the recording rows chapters read.
+
+    lead_in is the output time the slice's vod clip plays before start_time,
+    from snapping its first frame back to a keyframe.
+    """
 
     start_time: float
     end_time: float
+    lead_in: float = 0.0
+
+
+def _lead_in(recording: Any) -> float:
+    """Output seconds a chapter source plays before its first wall second."""
+    return recording.lead_in if isinstance(recording, _ChapterWindow) else 0.0
 
 
 # Matches the setpts factor used in timelapse exports (e.g. setpts=0.04*PTS).
@@ -376,15 +388,18 @@ class RecordingExporter(threading.Thread):
     def _resolve_coverage(self) -> tuple[list[list[Any]], set[str], bool]:
         """Resolve the export range into the spans the VOD manifest will serve.
 
-        Delegates to the same coverage resolution the manifest builder
+        Delegates to the same coverage resolution and glitch nulling the
-        uses, so what we plan around and what nginx-vod emits agree by
+        manifest builder uses, so what we plan around and what nginx-vod
-        construction. Returns the spans (each [row, start, end, is_main]),
+        emits agree by construction. Returns the spans (each [row, start,
-        the known video codecs, and whether audio survives the range.
+        end, is_main]), the known video codecs, and whether audio survives
+        the range.
         Memoized: several stages of the export ask the same question, and
         the recordings backing a finished range do not change under us.
         """
         if self._coverage is None:
-            intervals = resolve_coverage(self.camera, self.start_time, self.end_time)
+            intervals = null_audio_glitches(
+                resolve_coverage(self.camera, self.start_time, self.end_time)
+            )
             self._coverage = (
                 build_spans(intervals, self.pinned_stream),
                 known_video_codecs(intervals),
@@ -433,17 +448,57 @@ class RecordingExporter(threading.Thread):
             # hand-off to stage around
             return True
 
-        spans, codecs, keep_audio = self._resolve_coverage()
+        _spans, codecs, keep_audio = self._resolve_coverage()
-        runs = self._stream_runs(spans)
+        runs = self._planned_stream_runs()
 
         # a range one stream covers end to end has nothing to hand off,
         # so it stays on the existing path however long it is
         if len(runs) < 2:
             return True
 
-        runs = [piece for run in runs for piece in self._split_long_run(run)]
         return self._stage_stream_runs(runs, codecs, keep_audio)
 
+    def _planned_stream_runs(self) -> list[StreamRun]:
+        """The runs a mixed range is staged as, one pinned vod playlist each."""
+        runs = self._stream_runs(self._merged_spans())
+
+        if len(runs) < 2:
+            return runs
+
+        return [piece for run in runs for piece in self._split_long_run(run)]
+
+    def _staged_chapter_windows(self) -> list[_ChapterWindow]:
+        """Chapter windows for the staged files as they were rendered.
+
+        Each staged run comes from its own pinned vod playlist, whose first
+        clip snaps back to the preceding keyframe, so a staged file runs up
+        to a GOP longer than its slice of the merged timeline. Planning each
+        run the way its playlist does carries that lead-in into the chapter
+        offsets instead of letting it accumulate at every hand-off.
+        """
+        windows: list[_ChapterWindow] = []
+
+        for run in self._planned_stream_runs():
+            intervals = null_audio_glitches(
+                resolve_coverage(self.camera, run.start_time, run.end_time)
+            )
+
+            for clip in realized_timeline(intervals, run.stream_type):
+                # a skipped clip is absent from the playlist and the file
+                if clip["duration"] <= 0:
+                    continue
+
+                span = clip["end_time"] - clip["start_time"]
+                windows.append(
+                    _ChapterWindow(
+                        clip["start_time"],
+                        clip["end_time"],
+                        max(0.0, clip["duration"] / 1000 - span),
+                    )
+                )
+
+        return windows
+
     def _stream_runs(self, spans: list[list[Any]]) -> list[StreamRun]:
         """Collapse the merged spans into contiguous runs of one stream type.
 
@@ -840,6 +895,8 @@ class RecordingExporter(threading.Thread):
             clipped_end = min(float(rec.end_time), float(self.end_time))
             if clipped_end <= clipped_start:
                 continue
+            # a staged window's keyframe lead-in plays before it
+            output_offset += _lead_in(rec)
             windows.append((clipped_start, clipped_end, output_offset))
             output_offset += clipped_end - clipped_start
 
@@ -987,9 +1044,13 @@ class RecordingExporter(threading.Thread):
             if duration_ms <= 0:
                 continue
 
-            title = datetime.datetime.fromtimestamp(clipped_start, tz=tz).isoformat(
+            # a staged window's keyframe lead-in opens its chapter, with
-                timespec="seconds"
+            # frames captured that long before the window
-            )
+            lead_in = _lead_in(rec)
+            duration_ms += int(round(lead_in * 1000))
+            title = datetime.datetime.fromtimestamp(
+                clipped_start - lead_in, tz=tz
+            ).isoformat(timespec="seconds")
             chapter_blocks.append(
                 "[CHAPTER]\n"
                 "TIMEBASE=1/1000\n"
@@ -1128,12 +1189,12 @@ class RecordingExporter(threading.Thread):
         if self.staged_runs:
             # each run was already rendered to a temp file with a common
             # track timescale, so the concat demuxer has nothing left to
-            # reconcile and every chapter offset lines up with the merged
+            # reconcile
-            # timeline the staged files reproduce
+            recordings = (
-            recordings = [
+                self._staged_chapter_windows()
-                _ChapterWindow(span_start, span_end)
+                if self.chapters not in (None, ChaptersEnum.none)
-                for _row, span_start, span_end, _is_main in self._merged_spans()
+                else []
-            ]
+            )
             playlist_lines: list[str] = [f"file '{path}'" for path in self.staged_runs]
             ffmpeg_input = (
                 "-y -protocol_whitelist pipe,file -f concat -safe 0 -i /dev/stdin"
@@ -1149,11 +1210,18 @@ class RecordingExporter(threading.Thread):
             # its own rows are the ones the chapters describe
             recordings = self._get_recordings_for_range(pin)
         else:
-            # never mix streams in one playlist; use main when available
+            # an unstaged auto range resolves to at most one stream run, and
-            # and fall back to sub for expired-main history
+            # its rows are the ones the chapters describe. Main rows the
-            recordings = self._get_recordings_for_range(STREAM_TYPE_MAIN)
+            # manifest drops (glitches, slivers at the edges of a sub range)
+            # must not stand in for it.
+            runs = self._stream_runs(self._merged_spans())
+            recordings = self._get_recordings_for_range(
+                runs[0].stream_type if runs else STREAM_TYPE_MAIN
+            )
 
-            if not recordings:
+            # never mix streams in one playlist; fall back to sub for
+            # expired-main history
+            if not recordings and not runs:
                 recordings = self._get_recordings_for_range(STREAM_TYPE_SUB)
 
         playlist_lines = []
@@ -1307,7 +1375,7 @@ class RecordingExporter(threading.Thread):
 
             if preview.end_time > self.end_time:
                 playlist_lines.append(
-                    f"outpoint {int(preview.end_time - self.end_time)}"
+                    f"outpoint {int(self.end_time - preview.start_time)}"
                 )
 
         ffmpeg_input = (

frigate/record/maintainer.py

@@ -490,9 +490,17 @@ class RecordingMaintainer(threading.Thread):
                 )
             reviews = reviews_by_camera[camera]
 
-            tasks.extend(
+            # probes run concurrently, but each segment's start chains off the
-                [self.validate_and_move_segment(camera, reviews, r) for r in recordings]
+            # previous segment's end, so starts resolve in segment order
+            previous: asyncio.Event | None = None
+            for recording in recordings:
+                resolved = asyncio.Event()
+                tasks.append(
+                    self._validate_in_order(
+                        camera, reviews, recording, previous, resolved
                     )
+                )
+                previous = resolved
 
             # publish most recently available recording time and None if disabled
             if stream_type == STREAM_TYPE_MAIN:
@@ -550,12 +558,33 @@ class RecordingMaintainer(threading.Thread):
                 while info and info[0][0] < expire_before:
                     info.pop(0)
 
+    async def _validate_in_order(
+        self,
+        camera: str,
+        reviews: Any,
+        recording: dict[str, Any],
+        previous_start: asyncio.Event | None,
+        start_resolved: asyncio.Event,
+    ) -> dict[str, Any] | None:
+        """Validate a segment, always releasing the next one in its stream."""
+        try:
+            return await self.validate_and_move_segment(
+                camera, reviews, recording, previous_start, start_resolved
+            )
+        finally:
+            start_resolved.set()
+
     def drop_segment(self, cache_path: str) -> None:
         Path(cache_path).unlink(missing_ok=True)
         self.end_time_cache.pop(cache_path, None)
 
     async def validate_and_move_segment(
-        self, camera: str, reviews: Any, recording: dict[str, Any]
+        self,
+        camera: str,
+        reviews: Any,
+        recording: dict[str, Any],
+        previous_start: asyncio.Event | None = None,
+        start_resolved: asyncio.Event | None = None,
     ) -> dict[str, Any] | None:
         cache_path: str = recording["cache_path"]
         start_time: datetime.datetime = recording["start_time"]
@@ -617,6 +646,9 @@ class RecordingMaintainer(threading.Thread):
                 async with self.probe_semaphore:
                     keyframes = await get_keyframe_offsets(cache_path)
 
+                if previous_start is not None:
+                    await previous_start.wait()
+
                 start_time = self._resolve_segment_start(
                     camera, stream_type, start_time, duration, cache_path
                 )
@@ -654,6 +686,27 @@ class RecordingMaintainer(threading.Thread):
                 RecordingsDataTypeEnum.valid.value,
             )
 
+        # the start is settled, so the next segment of the stream can chain
+        # off it while this one waits on retention and the move
+        if start_resolved is not None:
+            start_resolved.set()
+
+        # assume that empty means the relevant recording info has not been received yet
+        camera_info = self.object_recordings_info[camera]
+        most_recently_processed_frame_time = (
+            camera_info[-1][0] if len(camera_info) > 0 else 0
+        )
+
+        # ensure delayed segment info does not lead to lost segments, every
+        # retention decision below depends on complete stats for the segment
+        if (
+            datetime.datetime.fromtimestamp(
+                most_recently_processed_frame_time
+            ).astimezone(datetime.UTC)
+            < end_time
+        ):
+            return None
+
         record_config = self.config.cameras[camera].record
 
         # sub's alerts/detections carry the retain mode directly, unlike
@@ -681,23 +734,8 @@ class RecordingMaintainer(threading.Thread):
         # we should first just check if this segment matches that
         # and avoid any DB calls
         if highest is not None:
-            # assume that empty means the relevant recording info has not been received yet
-            camera_info = self.object_recordings_info[camera]
-            most_recently_processed_frame_time = (
-                camera_info[-1][0] if len(camera_info) > 0 else 0
-            )
-
-            # ensure delayed segment info does not lead to lost segments
-            if (
-                datetime.datetime.fromtimestamp(
-                    most_recently_processed_frame_time
-                ).astimezone(datetime.UTC)
-                >= end_time
-            ):
             record_mode = (
-                    RetainModeEnum.all
+                RetainModeEnum.all if highest == "continuous" else RetainModeEnum.motion
-                    if highest == "continuous"
-                    else RetainModeEnum.motion
             )
             segment_stats = self.segment_stats(camera, start_time, end_time)
 
@@ -779,10 +817,6 @@ class RecordingMaintainer(threading.Thread):
         # continuous/motion retention (either disabled or segment_stats said
         # discard), so waiting longer just fills the cache.
         else:
-            camera_info = self.object_recordings_info[camera]
-            most_recently_processed_frame_time = (
-                camera_info[-1][0] if len(camera_info) > 0 else 0
-            )
             retain_cutoff = datetime.datetime.fromtimestamp(
                 most_recently_processed_frame_time - record_config.event_pre_capture
             ).astimezone(datetime.UTC)
@@ -1090,6 +1124,7 @@ class RecordingMaintainer(threading.Thread):
                 elif (
                     topic == DetectionTypeEnum.api.value
                     or topic == DetectionTypeEnum.lpr.value
+                    or topic == DetectionTypeEnum.classification_state.value
                 ):
                     continue
 

frigate/review/maintainer.py

@@ -40,6 +40,10 @@ logger = logging.getLogger(__name__)
 THUMB_HEIGHT = 180
 THUMB_WIDTH = 320
 
+# seconds before a review item starts that a state classification change is
+# still attached to it, e.g. a garage door opening before the car is visible
+CLASSIFICATION_STATE_PRE_ROLL = 5
+
 
 class PendingReviewSegment:
     def __init__(
@@ -61,6 +65,10 @@ class PendingReviewSegment:
         self.sub_labels = sub_labels
         self.zones = zones
         self.audio = audio
+        self.classification_state_changes: list[dict[str, Any]] = []
+        # detection-level activity after the last alert activity, split by the
+        # detection cutoff, these are published when the alert is cut off
+        self.pending_detections: list[PendingReviewSegment] = []
         self.thumb_time: float | None = None
         self.last_alert_time: float | None = None
         self.last_detection_time: float = frame_time
@@ -78,6 +86,20 @@ class PendingReviewSegment:
             CLIPS_DIR, f"review/thumb-{self.camera}-{self.id}.webp"
         )
 
+    def add_object(self, obj: dict[str, Any], attributes: list[str]) -> None:
+        """Add a tracked object's label, sub label, and zones to the segment."""
+        if not obj["sub_label"]:
+            self.detections[obj["id"]] = obj["label"]
+        elif obj["sub_label"][0] in attributes:
+            self.detections[obj["id"]] = obj["sub_label"][0]
+        else:
+            self.detections[obj["id"]] = f"{obj['label']}-verified"
+            self.sub_labels[obj["id"]] = obj["sub_label"][0]
+
+        for zone in obj["current_zones"]:
+            if zone not in self.zones:
+                self.zones.append(zone)
+
     def update_frame(
         self,
         camera_config: CameraConfig,
@@ -162,6 +184,7 @@ class PendingReviewSegment:
                     "sub_labels": list(self.sub_labels.values()),
                     "zones": self.zones,
                     "audio": list(self.audio),
+                    "classification_state_changes": self.classification_state_changes,
                     "thumb_time": self.thumb_time,
                     "metadata": None,
                 },
@@ -293,6 +316,9 @@ class ReviewSegmentMaintainer(threading.Thread):
         # manual events
         self.indefinite_events: dict[str, dict[str, Any]] = {}
 
+        # state classification changes seen while a camera had no review item
+        self.recent_classification_state_changes: dict[str, list[dict[str, Any]]] = {}
+
         # ensure dirs
         Path(os.path.join(CLIPS_DIR, "review")).mkdir(exist_ok=True)
 
@@ -374,6 +400,43 @@ class ReviewSegmentMaintainer(threading.Thread):
         self.active_review_segments[segment.camera] = None
         return end_time
 
+    def _activate_segment(self, segment: PendingReviewSegment) -> None:
+        """Make a segment the camera's active one, attaching any state
+        classification changes seen just before it started."""
+        self.active_review_segments[segment.camera] = segment
+        recent = self.recent_classification_state_changes.pop(segment.camera, [])
+        segment.classification_state_changes.extend(
+            c
+            for c in recent
+            if c["timestamp"] >= segment.start_time - CLASSIFICATION_STATE_PRE_ROLL
+        )
+
+    def handle_classification_state_change(
+        self, camera: str, change: dict[str, Any]
+    ) -> None:
+        """Attach a verified state classification change to the active segment.
+
+        State changes never start, extend, or upgrade a segment. A change seen
+        with no active segment is held briefly for a segment starting right
+        after it.
+        """
+        segment = self.active_review_segments.get(camera)
+
+        if segment is None:
+            cutoff = change["timestamp"] - CLASSIFICATION_STATE_PRE_ROLL
+            self.recent_classification_state_changes[camera] = [
+                c
+                for c in self.recent_classification_state_changes.get(camera, [])
+                if c["timestamp"] >= cutoff
+            ] + [change]
+            return
+
+        prev_data = segment.get_data(False)
+        segment.classification_state_changes.append(change)
+        self._publish_segment_update(
+            segment, self.config.cameras[camera], None, [], prev_data
+        )
+
     def forcibly_end_segment(self, camera: str) -> Any:
         """Forcibly end the pending segment for a camera."""
         segment = self.active_review_segments.get(camera)
@@ -389,9 +452,29 @@ class ReviewSegmentMaintainer(threading.Thread):
                     segment.last_detection_time = now
 
             prev_data = segment.get_data(False)
-            return self._publish_segment_end(segment, prev_data)
+            end_time = self._publish_segment_end(segment, prev_data)
+            self._publish_pending_detections(segment, None)
+            return end_time
         return None
 
+    def _publish_pending_detections(
+        self, segment: PendingReviewSegment, ongoing_since: float | None
+    ) -> None:
+        """Publish the detections held while an ended alert was active.
+
+        A detection with activity after ongoing_since stays open, only the
+        latest can. With None every detection is ended, this does not read the
+        camera config since a removed camera is no longer in it.
+        """
+        for pending in segment.pending_detections:
+            self._activate_segment(pending)
+            self._publish_segment_start(pending)
+
+            if ongoing_since is None or pending.last_detection_time < ongoing_since:
+                self._publish_segment_end(pending, pending.get_data(False))
+
+        segment.pending_detections = []
+
     def get_manual_event_severity(self, camera: str, label: str) -> SeverityEnum | None:
         """Determine the review severity for a manual event label.
 
@@ -422,6 +505,56 @@ class ReviewSegmentMaintainer(threading.Thread):
         """Close out a deleted camera's segment so a reused name cannot inherit it."""
         self.forcibly_end_segment(camera)
         self.indefinite_events.pop(camera, None)
+        self.recent_classification_state_changes.pop(camera, None)
+
+    def _track_pending_detection(
+        self,
+        segment: PendingReviewSegment,
+        camera_config: CameraConfig,
+        frame_name: str,
+        frame_time: float,
+        objects: list[dict[str, Any]],
+    ) -> None:
+        """Hold detection-level activity seen after an alert's last alert
+        activity, starting a separate detection when the gap since the previous
+        activity exceeds the detection cutoff."""
+        pending = segment.pending_detections[-1] if segment.pending_detections else None
+
+        if pending is None or frame_time > (
+            pending.last_detection_time + camera_config.review.detections.cutoff_time
+        ):
+            pending = PendingReviewSegment(
+                segment.camera,
+                frame_time,
+                SeverityEnum.detection,
+                {},
+                sub_labels={},
+                audio=set(),
+                zones=[],
+            )
+            segment.pending_detections.append(pending)
+
+        pending.last_detection_time = frame_time
+
+        for obj in objects:
+            pending.add_object(obj, self.config.all_attributes)
+
+        if len(objects) <= pending.frame_active_count:
+            return
+
+        try:
+            yuv_frame = self.frame_manager.get(
+                frame_name, camera_config.frame_shape_yuv
+            )
+        except FileNotFoundError:
+            return
+
+        if yuv_frame is None:
+            logger.debug(f"Failed to get frame {frame_name} from SHM")
+            return
+
+        pending.update_frame(camera_config, yuv_frame, objects)
+        self.frame_manager.close(frame_name)
 
     def update_existing_segment(
         self,
@@ -458,6 +591,21 @@ class ReviewSegmentMaintainer(threading.Thread):
                     should_update_state = True
                     should_update_image = True
 
+                # alert activity resumed, so the pending detection activity
+                # falls within this alert
+                for pending in segment.pending_detections:
+                    segment.detections.update(pending.detections)
+                    segment.sub_labels.update(pending.sub_labels)
+
+                    for zone in pending.zones:
+                        if zone not in segment.zones:
+                            segment.zones.append(zone)
+
+                    Path(pending.frame_path).unlink(missing_ok=True)
+                    should_update_state = True
+
+                segment.pending_detections = []
+
             if activity.has_activity_category(SeverityEnum.detection):
                 if (
                     segment.last_detection_time is None
@@ -465,6 +613,8 @@ class ReviewSegmentMaintainer(threading.Thread):
                 ):
                     segment.last_detection_time = frame_time
 
+            pending_objects: list[dict[str, Any]] = []
+
             for object in activity.get_all_objects():
                 # Alert-level objects should always be added (they extend/upgrade the segment)
                 # Detection-level objects should only be added if:
@@ -475,23 +625,22 @@ class ReviewSegmentMaintainer(threading.Thread):
 
                 if not is_alert_object and segment.severity == SeverityEnum.alert:
                     # This is a detection-level object
+                    if (
+                        segment.last_alert_time is not None
+                        and frame_time > segment.last_alert_time
+                    ):
+                        pending_objects.append(object)
+
                     # Only add if it started during the alert's active period
                     if object["start_time"] > segment.last_alert_time:
                         continue
 
-                if not object["sub_label"]:
+                segment.add_object(object, self.config.all_attributes)
-                    segment.detections[object["id"]] = object["label"]
-                elif object["sub_label"][0] in self.config.all_attributes:
-                    segment.detections[object["id"]] = object["sub_label"][0]
-                else:
-                    segment.detections[object["id"]] = f"{object['label']}-verified"
-                    segment.sub_labels[object["id"]] = object["sub_label"][0]
 
-                # keep zones up to date
+            if pending_objects:
-                if len(object["current_zones"]) > 0:
+                self._track_pending_detection(
-                    for zone in object["current_zones"]:
+                    segment, camera_config, frame_name, frame_time, pending_objects
-                        if zone not in segment.zones:
+                )
-                            segment.zones.append(zone)
 
             if len(activity.get_all_objects()) > segment.frame_active_count:
                 should_update_state = True
@@ -543,48 +692,26 @@ class ReviewSegmentMaintainer(threading.Thread):
                 except FileNotFoundError:
                     return
 
+        # detection-level activity must not keep an alert open, it continues
+        # in a new detection segment once the alert is cut off
         if (
             segment.severity == SeverityEnum.alert
             and segment.last_alert_time is not None
             and frame_time
             > (segment.last_alert_time + camera_config.review.alerts.cutoff_time)
         ):
-                needs_new_detection = (
+            self._publish_segment_end(segment, prev_data)
-                    segment.last_detection_time > segment.last_alert_time
+            self._publish_pending_detections(
-                    and (
+                segment, frame_time - camera_config.review.detections.cutoff_time
+            )
+        elif (
+            not has_activity
+            and segment.severity == SeverityEnum.detection
+            and frame_time
+            > (
                 segment.last_detection_time
                 + camera_config.review.detections.cutoff_time
             )
-                    > frame_time
-                )
-                last_detection_time = segment.last_detection_time
-
-                end_time = self._publish_segment_end(segment, prev_data)
-
-                if needs_new_detection:
-                    new_detections: dict[str, str] = {}
-                    new_zones = set()
-
-                    for o in activity.categorized_objects["detections"]:
-                        new_detections[o["id"]] = o["label"]
-                        new_zones.update(o["current_zones"])
-
-                    if new_detections:
-                        new_segment = PendingReviewSegment(
-                            segment.camera,
-                            end_time,
-                            SeverityEnum.detection,
-                            new_detections,
-                            sub_labels={},
-                            audio=set(),
-                            zones=list(new_zones),
-                        )
-                        self.active_review_segments[segment.camera] = new_segment
-                        self._publish_segment_start(new_segment)
-                        new_segment.last_detection_time = last_detection_time
-            elif segment.severity == SeverityEnum.detection and frame_time > (
-                segment.last_detection_time
-                + camera_config.review.detections.cutoff_time
         ):
             self._publish_segment_end(segment, prev_data)
 
@@ -639,7 +766,7 @@ class ReviewSegmentMaintainer(threading.Thread):
                     audio=set(),
                     zones=zones,
                 )
-                self.active_review_segments[camera] = new_segment
+                self._activate_segment(new_segment)
 
                 try:
                     yuv_frame = self.frame_manager.get(
@@ -713,6 +840,10 @@ class ReviewSegmentMaintainer(threading.Thread):
 
                 if camera not in self.indefinite_events:
                     self.indefinite_events[camera] = {}
+            elif topic == DetectionTypeEnum.classification_state.value:
+                (camera, classification_change) = data
+            else:
+                continue
 
             if camera not in self.config.cameras:
                 continue
@@ -723,6 +854,10 @@ class ReviewSegmentMaintainer(threading.Thread):
             ):
                 continue
 
+            if topic == DetectionTypeEnum.classification_state:
+                self.handle_classification_state_change(camera, classification_change)
+                continue
+
             current_segment = self.active_review_segments.get(camera)
 
             # Check if the current segment should be processed based on enabled settings
@@ -864,7 +999,8 @@ class ReviewSegmentMaintainer(threading.Thread):
                                 severity = SeverityEnum.detection
 
                     if severity:
-                        self.active_review_segments[camera] = PendingReviewSegment(
+                        self._activate_segment(
+                            PendingReviewSegment(
                                 camera,
                                 frame_time,
                                 severity,
@@ -873,6 +1009,7 @@ class ReviewSegmentMaintainer(threading.Thread):
                                 [],
                                 detections,
                             )
+                        )
                 elif topic == DetectionTypeEnum.api:
                     severity = self.get_manual_event_severity(
                         camera, manual_info["label"]
@@ -888,7 +1025,7 @@ class ReviewSegmentMaintainer(threading.Thread):
                             [],
                             set(),
                         )
-                        self.active_review_segments[camera] = api_segment
+                        self._activate_segment(api_segment)
 
                         if manual_info["state"] == ManualEventState.start:
                             self.indefinite_events[camera][manual_info["event_id"]] = (
@@ -915,7 +1052,7 @@ class ReviewSegmentMaintainer(threading.Thread):
                             [],
                             set(),
                         )
-                        self.active_review_segments[camera] = lpr_segment
+                        self._activate_segment(lpr_segment)
 
                         if manual_info["state"] == ManualEventState.start:
                             self.indefinite_events[camera][manual_info["event_id"]] = (

frigate/stats/hardware.py

@@ -256,6 +256,17 @@ class HardwareStats:
         )
         self.update_config()
 
+    def set_config(self, config: FrigateConfig) -> None:
+        """Follow a runtime config swap and recalculate the monitored hardware.
+
+        The camera update subscriber has to follow too, or later camera updates
+        would land on the discarded config.
+        """
+        self.config = config
+        self._config_subscriber.config = config
+        self._config_subscriber.camera_configs = config.cameras
+        self.update_config()
+
     def update_config(self) -> None:
         """Recalculate all hardware that needs to be monitored from the config."""
         names = self._scan_ffmpeg() | self._scan_detectors() | self._scan_enrichments()

frigate/stats/util.py

@@ -111,15 +111,18 @@ def get_detector_stats(
 ) -> dict[str, dict[str, Any]]:
     """Get stats for all detectors, including temperatures based on detector type."""
     detector_stats: dict[str, dict[str, Any]] = {}
-    detector_type_indices: dict[str, int] = {}
+    # detector type -> device -> index into that type's temperatures
+    device_indices: dict[str, dict[str, int]] = {}
 
     for name, detector in stats_tracking["detectors"].items():
         pid = detector.detect_process.pid if detector.detect_process else None
         detector_type = detector.detector_config.type
 
-        # Keep track of the index for each detector type to match temperatures correctly
+        # temperatures are per physical unit, so a repeated device
-        current_index = detector_type_indices.get(detector_type, 0)
+        # ("hailo:PCIe#2", see runner_names) shares its unit's reading
-        detector_type_indices[detector_type] = current_index + 1
+        device = name.partition("#")[0]
+        type_devices = device_indices.setdefault(detector_type, {})
+        current_index = type_devices.setdefault(device, len(type_devices))
 
         detector_stat = {
             "inference_speed": round(detector.avg_inference_speed.value * 1000, 2),  # type: ignore[attr-defined]
@@ -233,6 +236,15 @@ def skipped_percent(skipped_fps: float, camera_fps: float, enabled: bool) -> flo
     return round(skipped_fps / camera_fps * 100, 1)
 
 
+def get_go2rtc_pid(cpu_usages: dict[str, dict[str, Any]]) -> int | None:
+    """Find the pid of the running go2rtc process in the cpu usages."""
+    for pid, usage in cpu_usages.items():
+        if usage.get("cmdline", "").split(" ")[0].endswith("/go2rtc"):
+            return int(pid)
+
+    return None
+
+
 def stats_snapshot(
     config: FrigateConfig,
     stats_tracking: StatsTrackingTypes,
@@ -245,8 +257,9 @@ def stats_snapshot(
     total_camera_fps = total_process_fps = total_skipped_fps = total_detection_fps = 0
 
     stats["cameras"] = {}
-    for name, camera_stats in camera_metrics.items():
+    for name, camera_stats in list(camera_metrics.items()):
-        if name not in config.cameras:
+        camera_config = config.cameras.get(name)
+        if camera_config is None:
             continue
 
         total_camera_fps += camera_stats.camera_fps.value
@@ -263,7 +276,7 @@ def stats_snapshot(
         # Calculate connection quality based on current state
         # This is computed at stats-collection time so offline cameras
         # correctly show as unusable rather than excellent
-        expected_fps = config.cameras[name].detect.fps
+        expected_fps = camera_config.detect.fps
         current_fps = camera_stats.camera_fps.value
         reconnects = camera_stats.reconnects_last_hour.value
         stalls = camera_stats.stalls_last_hour.value
@@ -296,7 +309,7 @@ def stats_snapshot(
                 config.cameras[name].enabled,
             ),
             "detection_fps": round(camera_stats.detection_fps.value, 2),
-            "detection_enabled": config.cameras[name].detect.enabled,
+            "detection_enabled": camera_config.detect.enabled,
             "pid": pid,
             "capture_pid": capture_pid,
             "ffmpeg_pid": ffmpeg_pid,
@@ -352,6 +365,14 @@ def stats_snapshot(
 
     stats["service"]["storage"]["/dev/shm"] = calculate_shm_requirements(config)
 
+    cpu_usages = stats.get("cpu_usages", {})
+
+    # go2rtc is supervised by s6, so its pid changes when s6 restarts it
+    go2rtc_pid = get_go2rtc_pid(cpu_usages)
+
+    if go2rtc_pid is not None:
+        stats_tracking["processes"]["go2rtc"] = go2rtc_pid
+
     stats["processes"] = {}
     for name, pid in stats_tracking["processes"].items():
         stats["processes"][name] = {
@@ -360,7 +381,6 @@ def stats_snapshot(
 
     # Embed cpu/mem stats into detectors, cameras, and processes
     # so history consumers don't need the full cpu_usages dict
-    cpu_usages = stats.get("cpu_usages", {})
 
     for det_stats in stats["detectors"].values():
         pid_str = str(det_stats.get("pid", ""))

frigate/test/http_api/base_http_test.py

@@ -145,7 +145,11 @@ class BaseTestHttp(unittest.TestCase):
             pass
 
     def create_app(
-        self, stats=None, event_metadata_publisher=None, notice_registry=None
+        self,
+        stats=None,
+        event_metadata_publisher=None,
+        notice_registry=None,
+        enforce_default_admin=False,
     ):
         from frigate.api.auth import get_allowed_cameras_for_filter, get_current_user
 
@@ -160,7 +164,7 @@ class BaseTestHttp(unittest.TestCase):
             event_metadata_publisher,
             None,
             DebugReplayManager(),
-            enforce_default_admin=False,
+            enforce_default_admin=enforce_default_admin,
             notice_registry=notice_registry,
         )
 

frigate/test/http_api/test_http_app.py

@@ -1,8 +1,11 @@
+import json
+import os
 from unittest.mock import Mock, patch
 
 import frigate.genai
 from frigate.config import GenAIProviderEnum
-from frigate.const import REDACTED_CREDENTIAL_SENTINEL
+from frigate.config.env import FRIGATE_ENV_VARS
+from frigate.const import MODEL_CACHE_DIR, REDACTED_CREDENTIAL_SENTINEL
 from frigate.genai import GenAIClient
 from frigate.models import Event, Recordings, ReviewSegment
 from frigate.stats.emitter import StatsEmitter
@@ -47,6 +50,25 @@ class TestHttpApp(BaseTestHttp):
             assert response.status_code == 200
             assert response.json()["front_door"]["usage_percent"] == 25.0
 
+    def test_camera_name_collision_keeps_admin_default(self):
+        self.minimal_config["cameras"]["faces"] = self.minimal_config["cameras"].pop(
+            "front_door"
+        )
+        app = super().create_app(enforce_default_admin=True)
+        viewer = {"remote-user": "viewer", "remote-role": "viewer"}
+
+        with AuthTestClient(app) as client:
+            assert client.get("/faces", headers=viewer).status_code == 403
+            assert client.get("/faces").status_code == 200
+            assert (
+                client.post("/faces/train/person/classify", headers=viewer).status_code
+                == 403
+            )
+
+            # Camera routes for the same name stay reachable by viewers
+            response = client.get("/faces/recordings/summary", headers=viewer)
+            assert response.status_code == 200
+
     def test_config_set_in_memory_replaces_objects_track_list(self):
         self.minimal_config["cameras"]["front_door"]["objects"] = {
             "track": ["person", "car"],
@@ -90,6 +112,68 @@ class TestHttpApp(BaseTestHttp):
             mqtt = response.json()["mqtt"]
             assert mqtt["password"] == REDACTED_CREDENTIAL_SENTINEL
 
+    def test_config_response_hides_notification_email_from_viewers(self):
+        self.minimal_config["notifications"] = {"email": "{FRIGATE_TEST_EMAIL}"}
+
+        with patch.dict(FRIGATE_ENV_VARS, {"FRIGATE_TEST_EMAIL": "me@example.com"}):
+            app = super().create_app()
+
+        assert app.frigate_config.notifications.email == "me@example.com"
+
+        with AuthTestClient(app) as client:
+            response = client.get(
+                "/config",
+                headers={"remote-user": "viewer", "remote-role": "viewer"},
+            )
+            assert response.status_code == 200
+            config = response.json()
+            assert config["notifications"]["email"] == REDACTED_CREDENTIAL_SENTINEL
+            assert (
+                config["cameras"]["front_door"]["notifications"]["email"]
+                == REDACTED_CREDENTIAL_SENTINEL
+            )
+
+            response = client.get("/config")
+            assert response.json()["notifications"]["email"] == "me@example.com"
+
+    def test_config_response_keeps_plus_model_reference(self):
+        model_id = "test_plus_reference"
+        model_path = os.path.join(MODEL_CACHE_DIR, model_id)
+        os.makedirs(MODEL_CACHE_DIR, exist_ok=True)
+
+        with open(model_path, "w") as f:
+            f.write("model")
+
+        with open(f"{model_path}.json", "w") as f:
+            json.dump(
+                {
+                    "id": model_id,
+                    "type": "ssd",
+                    "supportedDetectors": ["cpu"],
+                    "width": 320,
+                    "height": 320,
+                    "inputShape": "nhwc",
+                    "pixelFormat": "rgb",
+                    "labelMap": {"0": "person"},
+                },
+                f,
+            )
+
+        self.addCleanup(os.remove, model_path)
+        self.addCleanup(os.remove, f"{model_path}.json")
+        self.minimal_config["models"] = [
+            {"path": f"plus://{model_id}", "devices": ["cpu"]}
+        ]
+        app = super().create_app()
+
+        with AuthTestClient(app) as client:
+            response = client.get("/config")
+            assert response.status_code == 200
+            assert response.json()["models"][0]["path"] == f"plus://{model_id}"
+
+        # detection still loads the resolved cache file
+        assert app.frigate_config.models[0].path == model_path
+
     ####################################################################################################################
     ###################################  POST /genai/probe Endpoint   ##################################################
     ####################################################################################################################

frigate/test/http_api/test_http_config_set_live.py

@@ -0,0 +1,173 @@
+"""Tests for config_set live stream ordering and go2rtc transcode sync."""
+
+import os
+import tempfile
+from unittest.mock import MagicMock, Mock, patch
+
+import ruamel.yaml
+from fastapi import Request
+
+from frigate.api.auth import get_allowed_cameras_for_filter, get_current_user
+from frigate.api.fastapi_app import create_fastapi_app
+from frigate.config import FrigateConfig
+from frigate.config.camera.updater import CameraConfigUpdatePublisher
+from frigate.models import Event, Recordings, ReviewSegment
+from frigate.test.http_api.base_http_test import AuthTestClient, BaseTestHttp
+from frigate.util.live_streams import transcode_stream_source
+
+STREAMS_PATH = "cameras.front_door.live.streams"
+
+
+class TestConfigSetLiveStreams(BaseTestHttp):
+    def setUp(self):
+        super().setUp(models=[Event, Recordings, ReviewSegment])
+        self.minimal_config["go2rtc"] = {
+            "streams": {
+                "front_main": ["rtsp://10.0.0.1:554/main"],
+                "front_sub": ["rtsp://10.0.0.1:554/sub"],
+            }
+        }
+        self.minimal_config["cameras"]["front_door"]["live"] = {
+            "streams": {"Main": "front_main", "Sub": "front_sub"}
+        }
+
+    def _write_config_file(self) -> str:
+        yaml = ruamel.yaml.YAML()
+        f = tempfile.NamedTemporaryFile(mode="w", suffix=".yml", delete=False)
+        yaml.dump(self.minimal_config, f)
+        f.close()
+        self.addCleanup(os.unlink, f.name)
+        return f.name
+
+    def _read_config_file(self, path: str) -> dict:
+        with open(path) as f:
+            return ruamel.yaml.YAML(typ="safe").load(f)
+
+    def _app(self):
+        publisher = Mock(spec=CameraConfigUpdatePublisher)
+        publisher.publisher = MagicMock()
+        app = create_fastapi_app(
+            FrigateConfig(**self.minimal_config),
+            self.db,
+            None,
+            None,
+            None,
+            None,
+            None,
+            None,
+            publisher,
+            None,
+            enforce_default_admin=False,
+        )
+
+        async def mock_get_current_user(request: Request):
+            return {
+                "username": request.headers.get("remote-user"),
+                "role": request.headers.get("remote-role"),
+            }
+
+        async def mock_get_allowed_cameras_for_filter(request: Request):
+            return ["front_door"]
+
+        app.dependency_overrides[get_current_user] = mock_get_current_user
+        app.dependency_overrides[get_allowed_cameras_for_filter] = (
+            mock_get_allowed_cameras_for_filter
+        )
+        return app
+
+    def _save(self, app, live: dict, replace_paths: list[str] | None = None):
+        body = {
+            "config_data": {"cameras": {"front_door": {"live": live}}},
+            "requires_restart": 0,
+            "update_topic": "config/cameras/front_door/live",
+        }
+
+        if replace_paths is not None:
+            body["replace_paths"] = replace_paths
+
+        with AuthTestClient(app) as client:
+            return client.put("/config/set", json=body)
+
+    @patch("frigate.api.app.find_config_file")
+    def test_replace_paths_saves_map_in_sent_order(self, mock_find_config):
+        path = self._write_config_file()
+        mock_find_config.return_value = path
+
+        resp = self._save(
+            self._app(),
+            {"streams": {"Sub": "front_sub", "Main": "front_main"}},
+            [STREAMS_PATH],
+        )
+
+        self.assertEqual(resp.status_code, 200)
+        streams = self._read_config_file(path)["cameras"]["front_door"]["live"][
+            "streams"
+        ]
+        self.assertEqual(list(streams), ["Sub", "Main"])
+
+    @patch("frigate.api.app.find_config_file")
+    def test_without_replace_paths_order_is_kept(self, mock_find_config):
+        path = self._write_config_file()
+        mock_find_config.return_value = path
+
+        self._save(self._app(), {"streams": {"Sub": "front_sub", "Main": "front_main"}})
+
+        streams = self._read_config_file(path)["cameras"]["front_door"]["live"][
+            "streams"
+        ]
+        self.assertEqual(list(streams), ["Main", "Sub"])
+
+    @patch("frigate.api.app.find_config_file")
+    def test_replace_path_missing_from_yaml_is_skipped(self, mock_find_config):
+        del self.minimal_config["cameras"]["front_door"]["live"]
+        path = self._write_config_file()
+        mock_find_config.return_value = path
+
+        resp = self._save(
+            self._app(), {"streams": {"Main": "front_main"}}, [STREAMS_PATH]
+        )
+
+        self.assertEqual(resp.status_code, 200)
+        self.assertEqual(
+            self._read_config_file(path)["cameras"]["front_door"]["live"]["streams"],
+            {"Main": "front_main"},
+        )
+
+    @patch("frigate.api.app.sync_transcode_streams", return_value=True)
+    @patch("frigate.api.app.find_config_file")
+    def test_enabling_transcode_syncs_go2rtc(self, mock_find_config, mock_sync):
+        path = self._write_config_file()
+        mock_find_config.return_value = path
+
+        resp = self._save(
+            self._app(),
+            {
+                "transcode": {
+                    "enabled": True,
+                    "qualities": [{"height": 480, "bitrate": 500}],
+                }
+            },
+        )
+
+        self.assertEqual(resp.status_code, 200)
+        self.assertTrue(resp.json()["go2rtc_synced"])
+        mock_sync.assert_called_once_with(
+            {},
+            {
+                "front_door_transcode_480p": transcode_stream_source(
+                    "front_main", 480, 500
+                )
+            },
+        )
+
+    @patch("frigate.api.app.sync_transcode_streams", return_value=False)
+    @patch("frigate.api.app.find_config_file")
+    def test_sync_failure_is_reported(self, mock_find_config, mock_sync):
+        path = self._write_config_file()
+        mock_find_config.return_value = path
+
+        resp = self._save(self._app(), {"transcode": {"enabled": True}})
+
+        self.assertEqual(resp.status_code, 200)
+        self.assertTrue(resp.json()["success"])
+        self.assertFalse(resp.json()["go2rtc_synced"])

frigate/test/http_api/test_http_event.py

@@ -168,6 +168,29 @@ class TestHttpApp(BaseTestHttp):
             assert events[0]["id"] == id
             assert events[1]["id"] == id2
 
+    def test_get_event_list_offset_pages_score_sort(self):
+        now = datetime.now().timestamp()
+        scores = [0.6, 0.9, 0.7, 0.95, 0.8]
+
+        with AuthTestClient(self.app) as client:
+            for i, score in enumerate(scores):
+                super().insert_mock_event(
+                    f"event-{i}", start_time=now + i, data={"score": score}
+                )
+
+            params = {"sort": "score_desc"}
+            full = [e["id"] for e in client.get("/events", params=params).json()]
+            paged = [
+                e["id"]
+                for offset in (0, 2, 4)
+                for e in client.get(
+                    "/events", params={**params, "limit": 2, "offset": offset}
+                ).json()
+            ]
+
+            assert full == ["event-3", "event-1", "event-4", "event-2", "event-0"]
+            assert paged == full
+
     def test_get_event_list_match_multilingual_attribute(self):
         event_id = "123456.zh"
         attribute = "中文标签"
@@ -219,6 +242,85 @@ class TestHttpApp(BaseTestHttp):
             assert len(events) == 1
             assert events[0]["id"] == event_id
 
+    def test_events_search_offset_pages_score_sort(self):
+        now = datetime.now().timestamp()
+        scores = [0.6, 0.9, 0.7, 0.95, 0.8]
+        ids = [f"event-{i}" for i in range(len(scores))]
+        mock_embeddings = Mock()
+        mock_embeddings.search_thumbnail.return_value = [
+            (event_id, 0.1 * i) for i, event_id in enumerate(ids)
+        ]
+
+        self.app.frigate_config.semantic_search.enabled = True
+        self.app.embeddings = mock_embeddings
+
+        with AuthTestClient(self.app) as client:
+            for i, score in enumerate(scores):
+                super().insert_mock_event(
+                    ids[i], start_time=now + i, data={"score": score}
+                )
+
+            params = {
+                "search_type": "similarity",
+                "event_id": ids[0],
+                "sort": "score_desc",
+            }
+            paged = [
+                e["id"]
+                for offset in (0, 2, 4)
+                for e in client.get(
+                    "/events/search",
+                    params={**params, "limit": 2, "offset": offset},
+                ).json()
+            ]
+
+            assert paged == ["event-3", "event-1", "event-4", "event-2", "event-0"]
+
+    def test_events_search_offset_pages_orders_ties_by_id(self):
+        now = datetime.now().timestamp()
+        ids = ["event-c", "event-a", "event-b"]
+        mock_embeddings = Mock()
+        mock_embeddings.search_thumbnail.return_value = [
+            (event_id, 0.1) for event_id in ids
+        ]
+
+        self.app.frigate_config.semantic_search.enabled = True
+        self.app.embeddings = mock_embeddings
+
+        with AuthTestClient(self.app) as client:
+            for i, event_id in enumerate(ids):
+                super().insert_mock_event(
+                    event_id, start_time=now + i, data={"score": 0.8}
+                )
+
+            for sort in ("score_desc", "relevance"):
+                params = {
+                    "search_type": "similarity",
+                    "event_id": ids[0],
+                    "sort": sort,
+                }
+                paged = [
+                    e["id"]
+                    for offset in (0, 1, 2)
+                    for e in client.get(
+                        "/events/search",
+                        params={**params, "limit": 1, "offset": offset},
+                    ).json()
+                ]
+
+                assert paged == ["event-a", "event-b", "event-c"]
+
+    def test_event_list_rejects_negative_offset(self):
+        with AuthTestClient(self.app) as client:
+            response = client.get("/events", params={"offset": -5})
+            assert response.status_code == 422
+
+            response = client.get(
+                "/events/search",
+                params={"query": "car", "offset": -5},
+            )
+            assert response.status_code == 422
+
     def test_similarity_search_hides_unauthorized_anchor_event(self):
         mock_embeddings = Mock()
         self.app.frigate_config.semantic_search.enabled = True

frigate/test/http_api/test_http_go2rtc_bitrate.py

@@ -0,0 +1,61 @@
+"""Tests for the go2rtc stream bitrate endpoint."""
+
+from unittest.mock import patch
+
+from frigate.test.http_api.base_http_test import AuthTestClient, BaseTestHttp
+
+
+class TestHttpGo2rtcBitrate(BaseTestHttp):
+    def setUp(self):
+        super().setUp([])
+        self.minimal_config["go2rtc"] = {
+            "streams": {"front_main": ["rtsp://10.0.0.1:554/main"]}
+        }
+        self.minimal_config["cameras"]["front_door"]["live"] = {
+            "streams": {"Main": "front_main"},
+            "transcode": {
+                "enabled": True,
+                "qualities": [{"height": 480, "bitrate": 500}],
+            },
+        }
+        self.app = self.create_app()
+
+    @patch("frigate.api.camera.measure_stream_bitrate", return_value=812.4)
+    def test_returns_rounded_kbps(self, mock_measure):
+        with AuthTestClient(self.app) as client:
+            resp = client.get("/go2rtc/streams/front_main/bitrate")
+
+        self.assertEqual(resp.status_code, 200)
+        self.assertEqual(resp.json()["kbps"], 812)
+        mock_measure.assert_called_once_with("front_main")
+
+    @patch("frigate.api.camera.measure_stream_bitrate", return_value=480.0)
+    def test_generated_streams_are_known(self, _):
+        with AuthTestClient(self.app) as client:
+            resp = client.get("/go2rtc/streams/front_door_transcode_480p/bitrate")
+
+        self.assertEqual(resp.status_code, 200)
+
+    @patch("frigate.api.camera.measure_stream_bitrate")
+    def test_unknown_stream_is_404(self, mock_measure):
+        with AuthTestClient(self.app) as client:
+            resp = client.get("/go2rtc/streams/rtsp_somewhere/bitrate")
+
+        self.assertEqual(resp.status_code, 404)
+        mock_measure.assert_not_called()
+
+    @patch("frigate.api.camera.measure_stream_bitrate", return_value=None)
+    def test_no_data_is_502(self, _):
+        with AuthTestClient(self.app) as client:
+            resp = client.get("/go2rtc/streams/front_main/bitrate")
+
+        self.assertEqual(resp.status_code, 502)
+
+    def test_viewer_is_forbidden(self):
+        with AuthTestClient(self.app) as client:
+            resp = client.get(
+                "/go2rtc/streams/front_main/bitrate",
+                headers={"remote-user": "viewer", "remote-role": "viewer"},
+            )
+
+        self.assertEqual(resp.status_code, 403)

frigate/test/http_api/test_http_review.py

@@ -240,9 +240,101 @@ class TestHttpReview(BaseTestHttp):
             assert len(response_json) == 1
             assert response_json[0]["id"] == id_reviewed
 
+    def test_get_review_with_label_filter_matches_verified(self):
+        """Test that a label filter also matches the `-verified` variant."""
+        now = datetime.now().timestamp()
+
+        with AuthTestClient(self.app) as client:
+            super().insert_mock_review_segment(
+                "123456.person", now, now + 2, data={"objects": ["person"]}
+            )
+            super().insert_mock_review_segment(
+                "123456.verified", now, now + 2, data={"objects": ["person-verified"]}
+            )
+            super().insert_mock_review_segment(
+                "123456.car", now, now + 2, data={"objects": ["car"]}
+            )
+
+            params = {
+                "labels": "person",
+                "after": now - 1,
+                "before": now + 3,
+            }
+            response = client.get("/review", params=params)
+            assert response.status_code == 200
+            response_json = response.json()
+            assert {r["id"] for r in response_json} == {
+                "123456.person",
+                "123456.verified",
+            }
+
+    def test_get_review_with_label_filter_does_not_match_prefix(self):
+        """Test that a label filter does not match labels that only share a prefix."""
+        now = datetime.now().timestamp()
+
+        with AuthTestClient(self.app) as client:
+            super().insert_mock_review_segment(
+                "123456.carrot", now, now + 2, data={"objects": ["carrot"]}
+            )
+
+            params = {
+                "labels": "car",
+                "after": now - 1,
+                "before": now + 3,
+            }
+            response = client.get("/review", params=params)
+            assert response.status_code == 200
+            assert len(response.json()) == 0
+
+    def test_get_review_with_audio_label_filter(self):
+        """Test that a label filter still matches audio labels."""
+        now = datetime.now().timestamp()
+
+        with AuthTestClient(self.app) as client:
+            super().insert_mock_review_segment(
+                "123456.audio", now, now + 2, data={"audio": ["speech"]}
+            )
+
+            params = {
+                "labels": "speech",
+                "after": now - 1,
+                "before": now + 3,
+            }
+            response = client.get("/review", params=params)
+            assert response.status_code == 200
+            response_json = response.json()
+            assert len(response_json) == 1
+            assert response_json[0]["id"] == "123456.audio"
+
     ####################################################################################################################
     ###################################  GET /review/summary Endpoint   #################################################
     ####################################################################################################################
+    def test_get_review_summary_label_filter_matches_verified(self):
+        """Test that the summary label filter also matches the `-verified` variant."""
+        with AuthTestClient(self.app) as client:
+            super().insert_mock_review_segment(
+                "123456.verified", data={"objects": ["person-verified"]}
+            )
+            super().insert_mock_review_segment(
+                "123456.car", data={"objects": ["car"]}, severity=SeverityEnum.detection
+            )
+
+            params = {
+                "cameras": "front_door",
+                "labels": "person",
+                "zones": "all",
+                "timezone": "utc",
+            }
+            response = client.get("/review/summary", params=params)
+            assert response.status_code == 200
+            response_json = response.json()
+            assert response_json["last24Hours"]["total_alert"] == 1
+            assert response_json["last24Hours"]["total_detection"] == 0
+
+            today_formatted = datetime.today().strftime("%Y-%m-%d")
+            assert response_json[today_formatted]["total_alert"] == 1
+            assert response_json[today_formatted]["total_detection"] == 0
+
     def test_get_review_summary_all_filters(self):
         with AuthTestClient(self.app) as client:
             super().insert_mock_review_segment("123456.random")

frigate/test/test_camera_config_updater.py

@@ -9,7 +9,6 @@ from frigate.config.camera.updater import (
     CameraConfigUpdateSubscriber,
 )
 from frigate.const import SUB_CACHE_TAG
-from frigate.detectors.detector_config import SceneEnum
 
 
 def _build_scene_frigate_config(scene: str | None) -> FrigateConfig:
@@ -119,7 +118,7 @@ class TestRecordUpdateRecreatesFfmpegCmds(unittest.TestCase):
         subscriber = CameraConfigUpdateSubscriber(
             config, {}, [CameraConfigUpdateEnum.add, CameraConfigUpdateEnum.remove]
         )
-        assert config.model_for_camera("front_door").scene == SceneEnum.outdoor
+        assert config.model_for_camera("front_door").scene == "outdoor"
 
         subscriber.subscriber.check_for_update.side_effect = [
             ("config/cameras/front_door/remove", config.cameras["front_door"]),
@@ -136,7 +135,7 @@ class TestRecordUpdateRecreatesFfmpegCmds(unittest.TestCase):
         ]
         subscriber.check_for_updates()
 
-        assert config.model_for_camera("front_door").scene == SceneEnum.all
+        assert config.model_for_camera("front_door").scene == "default"
 
     def test_unchanged_record_update_keeps_existing_cmds(self):
         camera_config = _build_camera_config(sub_enabled=False)

frigate/test/test_camera_lifecycle.py

@@ -179,12 +179,16 @@ class TestReviewMaintainerRemoval(unittest.TestCase):
         maintainer = ReviewSegmentMaintainer.__new__(ReviewSegmentMaintainer)
         maintainer.active_review_segments = {"deleted_cam": MagicMock()}
         maintainer.indefinite_events = {"deleted_cam": {"1234.5-abcdef": 1.0}}
+        maintainer.recent_classification_state_changes = {
+            "deleted_cam": [{"model": "gate", "from": "a", "to": "b", "timestamp": 1.0}]
+        }
         maintainer.forcibly_end_segment = MagicMock()
 
         maintainer._handle_camera_removed("deleted_cam")
 
         maintainer.forcibly_end_segment.assert_called_once_with("deleted_cam")
         self.assertNotIn("deleted_cam", maintainer.indefinite_events)
+        self.assertNotIn("deleted_cam", maintainer.recent_classification_state_changes)
 
 
 class TestAutotrackerMoveQueue(unittest.TestCase):

frigate/test/test_camera_watchdog.py

@@ -59,7 +59,6 @@ def build_watchdog(
             MagicMock(),
         )
 
-    watchdog.requestor = MagicMock()
     return watchdog
 
 
@@ -108,8 +107,8 @@ class TestCameraWatchdogStreamHealth(unittest.TestCase):
     def test_status_goes_to_the_matching_role_topic(self):
         watchdog = self._build_watchdog()
 
-        watchdog._send_record_status(STREAM_TYPE_MAIN, "online", 100.0)
+        watchdog.record_status[STREAM_TYPE_MAIN].send("online", 100.0)
-        watchdog._send_record_status(STREAM_TYPE_SUB, "offline", 100.0)
+        watchdog.record_status[STREAM_TYPE_SUB].send("offline", 100.0)
 
         watchdog.requestor.send_data.assert_any_call(
             "front_door/status/record", "online"
@@ -121,9 +120,9 @@ class TestCameraWatchdogStreamHealth(unittest.TestCase):
     def test_status_is_cached_per_stream(self):
         watchdog = self._build_watchdog()
 
-        watchdog._send_record_status(STREAM_TYPE_MAIN, "online", 100.0)
+        watchdog.record_status[STREAM_TYPE_MAIN].send("online", 100.0)
-        watchdog._send_record_status(STREAM_TYPE_SUB, "online", 100.0)
+        watchdog.record_status[STREAM_TYPE_SUB].send("online", 100.0)
-        watchdog._send_record_status(STREAM_TYPE_MAIN, "online", 100.0)
+        watchdog.record_status[STREAM_TYPE_MAIN].send("online", 100.0)
 
         assert watchdog.requestor.send_data.call_count == 2
 

frigate/test/test_config.py

@@ -1,17 +1,18 @@
 import json
 import os
+import tempfile
 import unittest
 from copy import deepcopy
 from unittest.mock import patch
 
 import numpy as np
+import requests
 from pydantic import ValidationError
 from ruamel.yaml.constructor import DuplicateKeyError
 
 from frigate.config import BirdseyeModeEnum, FrigateConfig, RetainModeEnum
 from frigate.const import MODEL_CACHE_DIR
 from frigate.detectors import DetectorTypeEnum
-from frigate.detectors.detector_config import SceneEnum
 from frigate.detectors.device import build_detector_config, runner_names
 from frigate.util.builtin import deep_merge
 
@@ -68,7 +69,7 @@ class TestConfig(unittest.TestCase):
     def test_config_class(self):
         frigate_config = FrigateConfig(**self.minimal)
         model = frigate_config.primary_model
-        assert model.scene == SceneEnum.all
+        assert model.scene == "default"
         assert model.width == 320
         assert frigate_config.devices_for_model(model)[0].detector == (
             DetectorTypeEnum.cpu
@@ -140,8 +141,8 @@ class TestConfig(unittest.TestCase):
 
         frigate_config = FrigateConfig(**(deep_merge(config, self.minimal)))
 
-        assert frigate_config.model_for_camera("back").scene == SceneEnum.outdoor
+        assert frigate_config.model_for_camera("back").scene == "outdoor"
-        assert frigate_config.model_for_camera("front").scene == SceneEnum.indoor
+        assert frigate_config.model_for_camera("front").scene == "indoor"
         assert frigate_config.model_for_camera("back").width == 320
         assert frigate_config.model_for_camera("front").width == 300
 
@@ -177,7 +178,7 @@ class TestConfig(unittest.TestCase):
 
         frigate_config = FrigateConfig(**(deep_merge(config, self.minimal)))
 
-        assert frigate_config.model_for_camera("back").scene == SceneEnum.all
+        assert frigate_config.model_for_camera("back").scene == "default"
 
     @patch("frigate.detectors.detector_config.load_labels")
     def test_model_for_camera_resolves_camera_added_after_parse(self, mock_labels):
@@ -205,7 +206,7 @@ class TestConfig(unittest.TestCase):
         new_config = FrigateConfig(**(deep_merge(deepcopy(config), added)))
         frigate_config.cameras["new_cam"] = new_config.cameras["new_cam"]
 
-        assert frigate_config.model_for_camera("new_cam").scene == SceneEnum.outdoor
+        assert frigate_config.model_for_camera("new_cam").scene == "outdoor"
         assert frigate_config.model_for_camera("new_cam").width == 416
 
     @patch("frigate.detectors.detector_config.load_labels")
@@ -221,7 +222,7 @@ class TestConfig(unittest.TestCase):
         frigate_config = FrigateConfig(**(deep_merge(deepcopy(config), self.minimal)))
 
         # a caller racing a runtime remove may still name the popped camera
-        assert frigate_config.model_for_camera("removed").scene == SceneEnum.all
+        assert frigate_config.model_for_camera("removed").scene == "default"
 
     @patch("frigate.detectors.detector_config.load_labels")
     def test_camera_scene_without_a_model_or_a_default(self, mock_labels):
@@ -256,6 +257,124 @@ class TestConfig(unittest.TestCase):
         with self.assertRaises(ValidationError):
             FrigateConfig(**(deep_merge(config, self.minimal)))
 
+    @patch("frigate.detectors.detector_config.load_labels")
+    def test_scene_names_are_not_a_fixed_list(self, mock_labels):
+        mock_labels.return_value = {}
+        config = {
+            "models": [
+                {"devices": ["cpu"]},
+                {"scene": "garage_thermal", "devices": ["openvino:CPU"]},
+            ],
+            "cameras": {"back": {"detect": {"scene": "garage_thermal"}}},
+        }
+
+        frigate_config = FrigateConfig(**(deep_merge(config, self.minimal)))
+
+        assert frigate_config.model_for_camera("back").scene == "garage_thermal"
+
+    @patch("frigate.detectors.detector_config.load_labels")
+    def test_scene_names_must_be_simple_identifiers(self, mock_labels):
+        mock_labels.return_value = {}
+        config = {"models": [{"scene": "front yard", "devices": ["cpu"]}]}
+
+        with self.assertRaises(ValidationError):
+            FrigateConfig(**(deep_merge(config, self.minimal)))
+
+    def _two_scene_config(self, default_path: str, outdoor_path: str) -> dict:
+        return {
+            "models": [
+                {"path": default_path, "devices": ["openvino:CPU"]},
+                {"scene": "outdoor", "path": outdoor_path, "devices": ["openvino:GPU"]},
+            ],
+            "cameras": {"back": {"detect": {"scene": "outdoor"}}},
+        }
+
+    @patch("frigate.detectors.detector_config.load_labels")
+    def test_models_with_the_same_path_are_combined(self, mock_labels):
+        mock_labels.return_value = {}
+        config = self._two_scene_config("/etc/hosts", "/etc/hosts")
+
+        with self.assertLogs("frigate.config.config", level="WARNING") as logs:
+            frigate_config = FrigateConfig(**(deep_merge(config, self.minimal)))
+
+        assert len(frigate_config.models) == 1
+        model = frigate_config.primary_model
+        assert model.devices == ["openvino:CPU", "openvino:GPU"]
+        assert [d.raw for d in frigate_config.devices_for_model(model)] == [
+            "openvino:CPU",
+            "openvino:GPU",
+        ]
+        assert frigate_config.model_for_camera("back") is model
+        assert frigate_config.cameras["back"].detect.scene == "default"
+        assert any("same model file" in line for line in logs.output)
+
+    @patch("frigate.detectors.detector_config.load_labels")
+    def test_models_with_the_same_file_contents_are_combined(self, mock_labels):
+        mock_labels.return_value = {}
+
+        with tempfile.TemporaryDirectory() as temp_dir:
+            first = os.path.join(temp_dir, "model.onnx")
+            copy = os.path.join(temp_dir, "renamed.onnx")
+
+            for path in (first, copy):
+                with open(path, "wb") as f:
+                    f.write(b"same weights")
+
+            config = self._two_scene_config(first, copy)
+            frigate_config = FrigateConfig(**(deep_merge(config, self.minimal)))
+
+        assert len(frigate_config.models) == 1
+        assert frigate_config.model_for_camera("back").scene == "default"
+
+    @patch("frigate.detectors.detector_config.load_labels")
+    def test_models_with_different_files_are_kept_apart(self, mock_labels):
+        mock_labels.return_value = {}
+
+        with tempfile.TemporaryDirectory() as temp_dir:
+            first = os.path.join(temp_dir, "model.onnx")
+            other = os.path.join(temp_dir, "thermal.onnx")
+
+            for path, contents in ((first, b"visible"), (other, b"thermal")):
+                with open(path, "wb") as f:
+                    f.write(contents)
+
+            config = self._two_scene_config(first, other)
+            frigate_config = FrigateConfig(**(deep_merge(config, self.minimal)))
+
+        assert len(frigate_config.models) == 2
+        assert frigate_config.model_for_camera("back").scene == "outdoor"
+
+    @patch("frigate.detectors.detector_config.load_labels")
+    def test_combined_models_keep_the_default_model(self, mock_labels):
+        mock_labels.return_value = {}
+        config = {
+            "models": [
+                {"scene": "outdoor", "path": "/etc/hosts", "devices": ["openvino:GPU"]},
+                {"path": "/etc/hosts", "devices": ["openvino:CPU"]},
+            ],
+            "cameras": {"back": {"detect": {"scene": "outdoor"}}},
+        }
+
+        frigate_config = FrigateConfig(**(deep_merge(config, self.minimal)))
+
+        assert [model.scene for model in frigate_config.models] == ["default"]
+        assert frigate_config.primary_model.devices == ["openvino:CPU", "openvino:GPU"]
+        assert frigate_config.model_for_camera("back").scene == "default"
+
+    @patch("frigate.detectors.detector_config.load_labels")
+    def test_models_on_different_detectors_are_kept_apart(self, mock_labels):
+        mock_labels.return_value = {}
+        config = {
+            "models": [
+                {"path": "/etc/hosts", "devices": ["openvino:CPU"]},
+                {"scene": "outdoor", "path": "/etc/hosts", "devices": ["onnx"]},
+            ],
+        }
+
+        frigate_config = FrigateConfig(**(deep_merge(config, self.minimal)))
+
+        assert len(frigate_config.models) == 2
+
     @patch("frigate.detectors.detector_config.load_labels")
     def test_model_devices_must_share_a_detector(self, mock_labels):
         mock_labels.return_value = {}
@@ -1477,6 +1596,31 @@ class TestConfig(unittest.TestCase):
         frigate_config = FrigateConfig(**config)
         assert frigate_config.primary_model.merged_labelmap[0] == "amazon"
 
+    @patch(
+        "frigate.plus.PlusApi.get_model_download_url",
+        side_effect=requests.exceptions.ConnectionError,
+    )
+    def test_plus_unreachable_is_validation_error(self, _):
+        config = {
+            "mqtt": {"host": "mqtt"},
+            "models": [{"path": "plus://unreachable", "devices": ["cpu"]}],
+            "cameras": {
+                "back": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {
+                                "path": "rtsp://10.0.0.1:554/video",
+                                "roles": ["detect"],
+                            },
+                        ]
+                    },
+                }
+            },
+        }
+
+        with self.assertRaisesRegex(ValidationError, "Unable to connect to Frigate+"):
+            FrigateConfig(**config)
+
     def test_fails_on_invalid_role(self):
         config = {
             "mqtt": {"host": "mqtt"},

frigate/test/test_config_migration.py

@@ -20,7 +20,7 @@ class TestMigrateModels(unittest.TestCase):
     def test_single_cpu_detector(self):
         migrated = migrate_models({"detectors": {"cpu": {"type": "cpu"}}})
 
-        self.assertEqual(migrated["models"], [{"scene": "all", "devices": ["cpu"]}])
+        self.assertEqual(migrated["models"], [{"scene": "default", "devices": ["cpu"]}])
         self.assertNotIn("detectors", migrated)
 
     def test_model_settings_are_carried_over(self):
@@ -35,7 +35,7 @@ class TestMigrateModels(unittest.TestCase):
             migrated["models"],
             [
                 {
-                    "scene": "all",
+                    "scene": "default",
                     "path": "plus://abc",
                     "width": 320,
                     "devices": ["edgetpu:pci:0"],
@@ -257,12 +257,37 @@ class TestMigrateConfigFile(unittest.TestCase):
         self.assertEqual(migrated["models"][0]["devices"], ["openvino:GPU"])
         self.assertNotIn("detectors", migrated)
 
+    def test_top_level_changes_survive_later_steps(self):
+        # 0.16 adds detect and 0.17 splits genai, both at the top level
+        migrated = self._migrate(
+            "mqtt:\n"
+            "  enabled: false\n"
+            "genai:\n"
+            "  provider: ollama\n"
+            "  model: llava\n"
+            "  prompt: describe it\n"
+            "cameras: {}\n"
+            "version: 0.15-1\n"
+        )
+
+        self.assertEqual(migrated["version"], CURRENT_CONFIG_VERSION)
+        self.assertTrue(migrated["detect"]["enabled"])
+        self.assertEqual(migrated["objects"]["genai"], {"prompt": "describe it"})
+        self.assertEqual(
+            migrated["genai"]["default"],
+            {
+                "provider": "ollama",
+                "model": "llava",
+                "roles": ["descriptions", "chat"],
+            },
+        )
+
     def test_a_migrated_config_is_left_alone(self):
         migrated = self._migrate(
             "mqtt:\n"
             "  enabled: false\n"
             "models:\n"
-            "  - scene: all\n"
+            "  - scene: default\n"
             "    devices:\n"
             "      - openvino:GPU\n"
             "cameras: {}\n"
@@ -270,7 +295,7 @@ class TestMigrateConfigFile(unittest.TestCase):
         )
 
         self.assertEqual(
-            migrated["models"], [{"scene": "all", "devices": ["openvino:GPU"]}]
+            migrated["models"], [{"scene": "default", "devices": ["openvino:GPU"]}]
         )
         self.assertFalse(
             os.path.exists(os.path.join(self.temp_dir.name, "backup_config.yaml"))
@@ -298,7 +323,7 @@ class TestMigrateConfigFile(unittest.TestCase):
             "mqtt:\n"
             "  enabled: false\n"
             "models:\n"
-            "  - scene: all\n"
+            "  - scene: default\n"
             "    devices:\n"
             "      - hailo8l:PCIe\n"
             "cameras: {}\n"

frigate/test/test_config_util.py

@@ -26,6 +26,7 @@ class TestSwapRuntimeConfig(unittest.TestCase):
         app.genai_manager.update_config.assert_called_once_with(config)
         app.profile_manager.update_config.assert_called_once_with(config)
         self.assertIs(app.stats_emitter.config, config)
+        app.stats_emitter.hardware_stats.set_config.assert_called_once_with(config)
         self.assertIs(app.dispatcher.config, config)
         for comm in app.dispatcher.comms:
             self.assertIs(comm.config, config)

frigate/test/test_detector_stats.py

@@ -0,0 +1,39 @@
+"""Tests for per-detector stats."""
+
+import unittest
+from unittest.mock import MagicMock, patch
+
+from frigate.stats.util import get_detector_stats
+
+
+def _detector(detector_type: str) -> MagicMock:
+    detector = MagicMock()
+    detector.detector_config.type = detector_type
+    detector.avg_inference_speed.value = 0.01
+    detector.detection_start.value = 0.0
+    detector.detect_process.pid = 1
+    return detector
+
+
+class TestDetectorTemperatures(unittest.TestCase):
+    def test_repeated_device_shares_its_unit_temperature(self):
+        stats_tracking = {
+            "detectors": {
+                "hailo:PCIe": _detector("hailo8l"),
+                "hailo:PCIe#2": _detector("hailo8l"),
+                "hailo:PCIe:1": _detector("hailo8l"),
+            }
+        }
+
+        with patch(
+            "frigate.stats.util.get_hardware_temperatures", return_value=[50.0, 60.0]
+        ):
+            stats = get_detector_stats(stats_tracking)
+
+        self.assertEqual(stats["hailo:PCIe"]["temperature"], 50.0)
+        self.assertEqual(stats["hailo:PCIe#2"]["temperature"], 50.0)
+        self.assertEqual(stats["hailo:PCIe:1"]["temperature"], 60.0)
+
+
+if __name__ == "__main__":
+    unittest.main()

frigate/test/test_export.py

@@ -543,6 +543,56 @@ class TestPinnedStream(unittest.TestCase):
         self.assertFalse(any("/vod/front/main/" in token for token in cmd))
 
 
+class TestExportTimelineAlignment(unittest.TestCase):
+    def test_unstaged_auto_reads_the_stream_it_serves(self) -> None:
+        # a sub-only range whose main rows are glitches the manifest drops
+        exporter = _make_exporter([_span("/s1.mp4", 1_000, 1_040, False)], {"h264"})
+        streams: list[str] = []
+
+        def rows(stream: str) -> list:
+            streams.append(stream)
+            return [_FakeRow(f"/{stream}.mp4")]
+
+        exporter._get_recordings_for_range = rows  # type: ignore[method-assign]
+        exporter.get_record_export_command("/exports/out.mp4")
+
+        self.assertEqual(streams, ["sub"])
+
+    def test_staged_chapters_carry_keyframe_lead_in(self) -> None:
+        exporter = _make_exporter(
+            [
+                _span("/m1.mp4", 1_000, 1_020, True),
+                _span("/s1.mp4", 1_020, 1_040, False),
+            ],
+            {"h264"},
+        )
+        exporter.config.ui.timezone = None
+
+        # each run's vod clip snaps 1.5s back to a keyframe
+        def timeline(_intervals: list, stream: str) -> list[dict]:
+            start, end = (1_000, 1_020) if stream == "main" else (1_020, 1_040)
+            return [
+                {
+                    "start_time": start,
+                    "end_time": end,
+                    "duration": (end - start + 1.5) * 1000,
+                }
+            ]
+
+        with (
+            patch("frigate.record.export.resolve_coverage", return_value=[]),
+            patch("frigate.record.export.realized_timeline", side_effect=timeline),
+        ):
+            windows = exporter._staged_chapter_windows()
+
+        path = exporter._build_recording_segment_chapter_metadata_file(windows)
+        self.addCleanup(os.remove, path)
+        content = Path(path).read_text()
+
+        self.assertIn("START=0\nEND=21500", content)
+        self.assertIn("START=21500\nEND=43000", content)
+
+
 class TestStagedFileCleanup(unittest.TestCase):
     """A staged path must be tracked before ffmpeg can write to it."""
 

frigate/test/test_genai_prompts.py

@@ -7,6 +7,7 @@ from frigate.genai.prompts import (
     REVIEW_DESCRIPTION_FIELD_GUIDELINES,
     REVIEW_RESPONSE_STYLES,
     build_review_description_prompt,
+    build_review_summary_prompt,
     get_review_field_guidelines,
 )
 
@@ -83,5 +84,49 @@ class TestReviewResponseStyle(unittest.TestCase):
         )
 
 
+class TestClassificationStateChanges(unittest.TestCase):
+    def _build_prompt(self, **extra) -> str:
+        review_data = {
+            "camera": "Front Door",
+            "start": "Monday, 09:30 AM",
+            "duration": 25,
+            "zones": [],
+            "unified_objects": ["person"],
+            **extra,
+        }
+        return build_review_description_prompt(
+            review_data, [b"fake-image"], [], None, "activity context"
+        )
+
+    def test_no_changes_leaves_prompt_unchanged(self):
+        self.assertEqual(
+            self._build_prompt(classification_state_changes=[]),
+            self._build_prompt(),
+        )
+        self.assertNotIn("## State Changes", self._build_prompt())
+
+    def test_changes_are_listed_before_objects(self):
+        change = "front gate changed from closed to open, 12s into the activity"
+        prompt = self._build_prompt(classification_state_changes=[change])
+        self.assertIn(f"\n- {change}\n\n## Objects in Scene", prompt)
+        self.assertLess(
+            prompt.index("## Sequence Details"), prompt.index("## State Changes")
+        )
+
+    def test_summary_describes_state_changes_only_when_present(self):
+        event = {"title": "Person at door", "camera": "Front Door", "context": []}
+        without = build_review_summary_prompt(0, 3600, [event], None)
+        self.assertNotIn('"state_changes"', without)
+
+        context_event = {
+            **event,
+            "context": [
+                {"camera": "Driveway", "state_changes": ["gate changed from a to b"]}
+            ],
+        }
+        with_changes = build_review_summary_prompt(0, 3600, [context_event], None)
+        self.assertIn('- "state_changes"', with_changes)
+
+
 if __name__ == "__main__":
     unittest.main()

frigate/test/test_genai_providers.py

@@ -19,6 +19,8 @@ import unittest
 from types import SimpleNamespace
 from unittest.mock import AsyncMock, MagicMock, patch
 
+import requests
+
 from frigate.config import GenAIConfig, GenAIProviderEnum
 from frigate.genai import PROVIDERS, load_providers
 
@@ -566,7 +568,6 @@ class TestLlamaCppProvider(unittest.TestCase):
             "supports_audio": False,
             "supports_tools": False,
             "supports_reasoning": False,
-            "media_marker": "<__media__>",
         }
         cls = PROVIDERS[GenAIProviderEnum.llamacpp]
         with patch.object(cls, "_get_model_info", return_value=info):
@@ -589,6 +590,62 @@ class TestLlamaCppProvider(unittest.TestCase):
         with patch.object(client, "_fetch_models_data", return_value=models_data):
             self.assertEqual(client.list_models(), ["g4", "gemma", "qwen3-asr"])
 
+    @staticmethod
+    def _embeddings_response(vectors):
+        response = MagicMock()
+        response.status_code = 200
+        response.json.return_value = {
+            "object": "list",
+            "data": [
+                {"object": "embedding", "index": i, "embedding": v}
+                for i, v in enumerate(vectors)
+            ],
+        }
+        return response
+
+    def test_embed_posts_content_arrays_to_v1_embeddings(self):
+        client = self._client()
+        response = self._embeddings_response([[0.1] * 768, [0.2] * 768])
+
+        with patch.object(client, "_post", return_value=response) as post:
+            result = client.embed(texts=["a person"], images=[b"not an image"])
+
+        url = post.call_args.args[0]
+        payload = post.call_args.kwargs["json"]
+        self.assertEqual(url, "http://localhost:9999/v1/embeddings")
+        self.assertEqual(payload["model"], "m")
+        self.assertEqual(payload["encoding_format"], "float")
+        self.assertEqual(
+            payload["input"][0], {"content": [{"type": "text", "text": "a person"}]}
+        )
+        image_parts = payload["input"][1]["content"]
+        self.assertEqual(image_parts[0]["type"], "image_url")
+        self.assertEqual(
+            image_parts[0]["image_url"]["url"],
+            "data:image/jpeg;base64," + base64.b64encode(b"not an image").decode(),
+        )
+        self.assertEqual(image_parts[1], {"type": "text", "text": "\n"})
+        self.assertEqual(len(result), 2)
+        self.assertAlmostEqual(float(result[1][0]), 0.2, places=5)
+
+    def test_embed_normalizes_dimension(self):
+        client = self._client()
+        response = self._embeddings_response([[1.0] * 1024, [1.0] * 512])
+
+        with patch.object(client, "_post", return_value=response):
+            result = client.embed(texts=["long", "short"])
+
+        self.assertEqual([r.shape for r in result], [(768,), (768,)])
+        self.assertEqual(float(result[1][-1]), 0.0)
+
+    def test_embed_request_error_returns_empty(self):
+        client = self._client()
+        response = MagicMock()
+        response.raise_for_status.side_effect = requests.exceptions.HTTPError("400")
+
+        with patch.object(client, "_post", return_value=response):
+            self.assertEqual(client.embed(texts=["a"]), [])
+
 
 # ---------------------------------------------------------------------------
 # transcribe role
@@ -762,7 +819,6 @@ class TestLlamaCppTranscribe(unittest.TestCase):
             "supports_audio": supports_audio,
             "supports_tools": False,
             "supports_reasoning": False,
-            "media_marker": "<__media__>",
         }
         cls = PROVIDERS[GenAIProviderEnum.llamacpp]
         with patch.object(cls, "_get_model_info", return_value=info):

frigate/test/test_go2rtc_pid_stats.py

@@ -0,0 +1,70 @@
+"""Tests for resolving the go2rtc pid from cpu usages."""
+
+import unittest
+from types import SimpleNamespace
+from unittest.mock import Mock, patch
+
+from frigate.stats.util import get_go2rtc_pid, stats_snapshot
+
+
+class TestGo2rtcPid(unittest.TestCase):
+    def test_finds_go2rtc_by_binary_path(self):
+        cpu_usages = {
+            "frigate.full_system": {"cpu": "1.0", "mem": "2.0"},
+            "100": {"cmdline": "ffmpeg -i rtsp://127.0.0.1:8554/go2rtc_cam"},
+            "200": {
+                "cmdline": "/usr/local/go2rtc/bin/go2rtc -config=/dev/shm/go2rtc.yaml"
+            },
+            "300": {"cmdline": "frigate.recording"},
+        }
+
+        self.assertEqual(get_go2rtc_pid(cpu_usages), 200)
+
+    def test_finds_custom_go2rtc_binary(self):
+        self.assertEqual(get_go2rtc_pid({"42": {"cmdline": "/config/go2rtc"}}), 42)
+
+    def test_returns_none_when_go2rtc_is_not_running(self):
+        self.assertIsNone(get_go2rtc_pid({"100": {"cmdline": "ffmpeg -i x"}}))
+        self.assertIsNone(get_go2rtc_pid({}))
+
+
+class TestGo2rtcPidInSnapshot(unittest.TestCase):
+    def snapshot(self, tracking: dict, go2rtc_pid: int) -> dict:
+        def update_stats(stats: dict) -> None:
+            stats["cpu_usages"] = {
+                str(go2rtc_pid): {
+                    "cmdline": "/usr/local/go2rtc/bin/go2rtc -config=x",
+                    "cpu": str(go2rtc_pid / 100),
+                    "mem": str(go2rtc_pid / 10),
+                }
+            }
+
+        config = SimpleNamespace(
+            cameras={},
+            telemetry=SimpleNamespace(stats=SimpleNamespace(network_bandwidth=False)),
+        )
+        hardware_stats = Mock()
+        hardware_stats.update_stats.side_effect = update_stats
+
+        with (
+            patch("frigate.stats.util.get_detector_stats", return_value={}),
+            patch("frigate.stats.util.embeddings_stats", return_value={}),
+            patch("frigate.stats.util.calculate_shm_requirements", return_value={}),
+        ):
+            return stats_snapshot(config, tracking, hardware_stats)
+
+    def test_snapshot_follows_go2rtc_restart(self):
+        tracking = {
+            "camera_metrics": {},
+            "detectors": {},
+            "started": 0,
+            "latest_frigate_version": "",
+            "processes": {"go2rtc": 200, "recording": 50},
+            "storage_maintainer": None,
+        }
+
+        first = self.snapshot(tracking, 200)["processes"]["go2rtc"]
+        self.assertEqual(first, {"pid": 200, "cpu": "2.0", "mem": "20.0"})
+
+        restarted = self.snapshot(tracking, 300)["processes"]["go2rtc"]
+        self.assertEqual(restarted, {"pid": 300, "cpu": "3.0", "mem": "30.0"})

frigate/test/test_hardware_stats.py

@@ -288,6 +288,17 @@ class TestUpdateConfig(HardwareStatsTestCase):
 
         self.assertEqual(set(stats._monitored), {"rockchip"})
 
+    def test_follows_a_runtime_config_swap(self):
+        stats = self.make_stats(self.make_config())
+        self.assertEqual(set(stats._monitored), set())
+
+        swapped = self.make_config("preset-rk-h264")
+        stats.set_config(swapped)
+
+        self.assertEqual(set(stats._monitored), {"rockchip"})
+        self.assertIs(self.subscriber.return_value.config, swapped)
+        self.assertIs(self.subscriber.return_value.camera_configs, swapped.cameras)
+
 
 class TestUpdateStats(HardwareStatsTestCase):
     def run_stats(self, stats: HardwareStats) -> dict:

frigate/test/test_live_streams.py

@@ -0,0 +1,410 @@
+"""Tests for Frigate-managed go2rtc live streams."""
+
+import unittest
+from unittest.mock import MagicMock, call, patch
+
+import requests
+from pydantic import ValidationError
+
+from frigate.config import FrigateConfig
+from frigate.util.live_streams import (
+    DEFAULT_TRANSCODE_QUALITIES,
+    default_transcode_source,
+    generated_transcode_streams,
+    is_transcode_stream_name,
+    measure_stream_bitrate,
+    raw_transcode_streams,
+    sync_transcode_streams,
+    transcode_stream_name,
+    transcode_stream_source,
+)
+
+
+class TestTranscodeNaming(unittest.TestCase):
+    def test_stream_name(self):
+        self.assertEqual(transcode_stream_name("front", 720), "front_transcode_720p")
+
+    def test_is_transcode_stream_name(self):
+        self.assertTrue(is_transcode_stream_name("front", "front_transcode_480p"))
+        self.assertFalse(is_transcode_stream_name("front", "front_sub"))
+        self.assertFalse(is_transcode_stream_name("front", "back_transcode_480p"))
+        self.assertFalse(is_transcode_stream_name("front", "front_transcode_480"))
+
+    def test_source_gives_each_ffmpeg_token_its_own_raw_param(self):
+        self.assertEqual(
+            transcode_stream_source("front", 480, 500),
+            "ffmpeg:front#video=h264#height=480#hardware#audio=copy"
+            "#raw=-b:v#raw=500k#raw=-maxrate#raw=500k#raw=-bufsize#raw=1000k",
+        )
+
+    def test_default_source_skips_transcoded_streams(self):
+        streams = {"Low": "front_transcode_360p", "Main": "front_main"}
+        self.assertEqual(default_transcode_source("front", streams), "front_main")
+        self.assertIsNone(default_transcode_source("front", {}))
+
+
+class TestRawTranscodeStreams(unittest.TestCase):
+    def test_defaults_to_camera_stream_and_default_qualities(self):
+        config = {"cameras": {"front": {"live": {"transcode": {"enabled": True}}}}}
+        streams = raw_transcode_streams(config)
+
+        self.assertEqual(
+            list(streams),
+            [
+                transcode_stream_name("front", q["height"])
+                for q in DEFAULT_TRANSCODE_QUALITIES
+            ],
+        )
+        self.assertTrue(streams["front_transcode_720p"].startswith("ffmpeg:front#"))
+
+    def test_uses_first_live_stream_and_explicit_qualities(self):
+        config = {
+            "cameras": {
+                "front": {
+                    "live": {
+                        "streams": {"Main": "front_main", "Sub": "front_sub"},
+                        "transcode": {
+                            "enabled": True,
+                            "qualities": [{"height": 540, "bitrate": 800}],
+                        },
+                    }
+                }
+            }
+        }
+
+        self.assertEqual(
+            raw_transcode_streams(config),
+            {"front_transcode_540p": transcode_stream_source("front_main", 540, 800)},
+        )
+
+    def test_explicit_source_wins(self):
+        config = {
+            "cameras": {
+                "front": {
+                    "live": {
+                        "streams": {"Main": "front_main"},
+                        "transcode": {
+                            "enabled": True,
+                            "source": "front_sub",
+                            "qualities": [{"height": 360, "bitrate": 250}],
+                        },
+                    }
+                }
+            }
+        }
+
+        self.assertEqual(
+            raw_transcode_streams(config)["front_transcode_360p"],
+            transcode_stream_source("front_sub", 360, 250),
+        )
+
+    def test_malformed_qualities_are_skipped(self):
+        config = {
+            "cameras": {
+                "front": {
+                    "live": {
+                        "transcode": {
+                            "enabled": True,
+                            "qualities": [
+                                {"height": 720},
+                                "480",
+                                {"height": 360, "bitrate": 250},
+                            ],
+                        }
+                    }
+                },
+                "back": {
+                    "live": {"transcode": {"enabled": True, "qualities": {"a": 1}}}
+                },
+            }
+        }
+
+        self.assertEqual(list(raw_transcode_streams(config)), ["front_transcode_360p"])
+
+    def test_disabled_or_missing_generates_nothing(self):
+        self.assertEqual(raw_transcode_streams({}), {})
+        self.assertEqual(
+            raw_transcode_streams({"cameras": {"front": {"live": {}}}}), {}
+        )
+        self.assertEqual(
+            raw_transcode_streams(
+                {"cameras": {"front": {"live": {"transcode": {"enabled": False}}}}}
+            ),
+            {},
+        )
+
+
+def camera_config(live: dict | None = None, go2rtc: dict | None = None) -> dict:
+    config = {
+        "mqtt": {"host": "mqtt"},
+        "go2rtc": {
+            "streams": go2rtc
+            or {
+                "front": ["rtsp://10.0.0.1:554/main"],
+                "front_sub": ["rtsp://10.0.0.1:554/sub"],
+            }
+        },
+        "cameras": {
+            "front": {
+                "ffmpeg": {
+                    "inputs": [
+                        {"path": "rtsp://10.0.0.1:554/main", "roles": ["detect"]}
+                    ]
+                },
+                "detect": {"height": 1080, "width": 1920, "fps": 5},
+            }
+        },
+    }
+
+    if live is not None:
+        config["cameras"]["front"]["live"] = live
+
+    return config
+
+
+class TestLiveTranscodeConfig(unittest.TestCase):
+    def streams(self, live: dict, go2rtc: dict | None = None) -> dict[str, str]:
+        config = FrigateConfig(**camera_config(live, go2rtc))
+        return config.cameras["front"].live.streams
+
+    def test_global_transcode_is_ignored(self):
+        config = camera_config()
+        config["live"] = {"transcode": {"enabled": True}}
+
+        streams = FrigateConfig(**config).cameras["front"].live.streams
+        self.assertEqual(streams, {"front": "front"})
+
+    def test_disabled_leaves_streams_alone(self):
+        self.assertEqual(self.streams({}), {"front": "front"})
+
+    def test_enabled_appends_default_qualities(self):
+        streams = self.streams({"transcode": {"enabled": True}})
+
+        self.assertEqual(
+            list(streams.items()),
+            [
+                ("front", "front"),
+                ("720p", "front_transcode_720p"),
+                ("480p", "front_transcode_480p"),
+                ("360p", "front_transcode_360p"),
+            ],
+        )
+
+    def test_source_resolves_to_first_live_stream(self):
+        config = FrigateConfig(
+            **camera_config(
+                {
+                    "streams": {"Sub": "front_sub", "Main": "front"},
+                    "transcode": {"enabled": True},
+                }
+            )
+        )
+
+        self.assertEqual(config.cameras["front"].live.transcode.source, "front_sub")
+
+    def test_startup_and_validation_agree_on_inherited_source(self):
+        config = camera_config({"transcode": {"enabled": True}})
+        config["live"] = {"streams": {"Sub": "front_sub"}}
+
+        self.assertEqual(
+            raw_transcode_streams(config),
+            generated_transcode_streams(FrigateConfig(**config)),
+        )
+        self.assertEqual(
+            raw_transcode_streams(config)["front_transcode_360p"],
+            transcode_stream_source("front_sub", 360, 250),
+        )
+
+    def test_placed_entries_keep_position_and_label(self):
+        streams = self.streams(
+            {
+                "streams": {
+                    "Main": "front",
+                    "Low": "front_transcode_480p",
+                    "Sub": "front_sub",
+                },
+                "transcode": {"enabled": True},
+            }
+        )
+
+        self.assertEqual(
+            list(streams.items()),
+            [
+                ("Main", "front"),
+                ("Low", "front_transcode_480p"),
+                ("Sub", "front_sub"),
+                ("720p", "front_transcode_720p"),
+                ("360p", "front_transcode_360p"),
+            ],
+        )
+
+    def test_disabling_drops_transcoded_entries(self):
+        streams = self.streams(
+            {
+                "streams": {"Main": "front", "720p": "front_transcode_720p"},
+                "transcode": {"enabled": False},
+            }
+        )
+
+        self.assertEqual(streams, {"Main": "front"})
+
+    def test_changed_height_drops_the_old_entry(self):
+        streams = self.streams(
+            {
+                "streams": {"Main": "front", "720p": "front_transcode_720p"},
+                "transcode": {
+                    "enabled": True,
+                    "qualities": [{"height": 540, "bitrate": 800}],
+                },
+            }
+        )
+
+        self.assertEqual(streams, {"Main": "front", "540p": "front_transcode_540p"})
+
+    def test_real_go2rtc_stream_with_transcode_name_is_kept(self):
+        streams = self.streams(
+            {"streams": {"Main": "front", "Odd": "front_transcode_720p"}},
+            go2rtc={
+                "front": ["rtsp://10.0.0.1:554/main"],
+                "front_transcode_720p": ["rtsp://10.0.0.1:554/odd"],
+            },
+        )
+
+        self.assertEqual(streams, {"Main": "front", "Odd": "front_transcode_720p"})
+
+    def test_generated_name_colliding_with_go2rtc_stream_fails(self):
+        with self.assertRaises(ValidationError):
+            self.streams(
+                {"transcode": {"enabled": True}},
+                go2rtc={
+                    "front": ["rtsp://10.0.0.1:554/main"],
+                    "front_transcode_720p": ["rtsp://10.0.0.1:554/odd"],
+                },
+            )
+
+    def test_source_must_be_a_go2rtc_stream(self):
+        with self.assertRaises(ValidationError):
+            self.streams({"transcode": {"enabled": True, "source": "missing"}})
+
+    def test_duplicate_heights_fail(self):
+        with self.assertRaises(ValidationError):
+            self.streams(
+                {
+                    "transcode": {
+                        "enabled": True,
+                        "qualities": [
+                            {"height": 480, "bitrate": 500},
+                            {"height": 480, "bitrate": 600},
+                        ],
+                    }
+                }
+            )
+
+    def test_label_collision_fails(self):
+        with self.assertRaises(ValidationError):
+            self.streams(
+                {
+                    "streams": {"Main": "front", "720p": "front_sub"},
+                    "transcode": {"enabled": True},
+                }
+            )
+
+
+class TestGeneratedTranscodeStreams(unittest.TestCase):
+    def test_collects_enabled_cameras(self):
+        config = FrigateConfig(
+            **camera_config(
+                {
+                    "transcode": {
+                        "enabled": True,
+                        "qualities": [{"height": 480, "bitrate": 500}],
+                    }
+                }
+            )
+        )
+
+        self.assertEqual(
+            generated_transcode_streams(config),
+            {"front_transcode_480p": transcode_stream_source("front", 480, 500)},
+        )
+
+    def test_disabled_is_empty(self):
+        self.assertEqual(
+            generated_transcode_streams(FrigateConfig(**camera_config())), {}
+        )
+
+
+class TestSyncTranscodeStreams(unittest.TestCase):
+    @patch("frigate.util.live_streams.requests.request")
+    def test_puts_added_and_changed_and_deletes_removed(self, mock_request):
+        mock_request.return_value = MagicMock(ok=True)
+
+        ok = sync_transcode_streams(
+            {"a": "src-a", "b": "src-b", "c": "src-c"},
+            {"a": "src-a", "b": "src-b2", "d": "src-d"},
+        )
+
+        self.assertTrue(ok)
+        url = "http://127.0.0.1:1984/api/streams"
+        self.assertCountEqual(
+            mock_request.call_args_list,
+            [
+                call("put", url, params={"name": "b", "src": "src-b2"}, timeout=5),
+                call("put", url, params={"name": "d", "src": "src-d"}, timeout=5),
+                call("delete", url, params={"src": "c"}, timeout=5),
+            ],
+        )
+
+    @patch("frigate.util.live_streams.requests.request")
+    def test_unchanged_makes_no_calls(self, mock_request):
+        self.assertTrue(sync_transcode_streams({"a": "x"}, {"a": "x"}))
+        mock_request.assert_not_called()
+
+    @patch("frigate.util.live_streams.requests.request")
+    def test_failure_returns_false_and_keeps_going(self, mock_request):
+        mock_request.side_effect = [
+            requests.ConnectionError("down"),
+            MagicMock(ok=True),
+        ]
+
+        self.assertFalse(sync_transcode_streams({}, {"a": "x", "b": "y"}))
+        self.assertEqual(mock_request.call_count, 2)
+
+
+class TestMeasureStreamBitrate(unittest.TestCase):
+    @patch("frigate.util.live_streams.time.monotonic")
+    @patch("frigate.util.live_streams.requests.get")
+    def test_skips_warmup_and_averages(self, mock_get, mock_monotonic):
+        response = MagicMock(ok=True)
+        response.iter_content.return_value = [b"x" * 1000] * 6
+        mock_get.return_value.__enter__.return_value = response
+        # first byte, warmup, window start, then counted chunks up to 6 s
+        mock_monotonic.side_effect = [0.0, 0.5, 1.0, 2.0, 4.0, 7.0]
+
+        kbps = measure_stream_bitrate("front", duration=6.0, warmup=1.0)
+
+        self.assertAlmostEqual(kbps, 3000 * 8 / 1000 / 6.0)
+        mock_get.assert_called_once_with(
+            "http://127.0.0.1:1984/api/stream.mp4",
+            params={"src": "front"},
+            stream=True,
+            timeout=5,
+        )
+
+    @patch("frigate.util.live_streams.requests.get")
+    def test_no_data_returns_none(self, mock_get):
+        response = MagicMock(ok=True)
+        response.iter_content.return_value = []
+        mock_get.return_value.__enter__.return_value = response
+
+        self.assertIsNone(measure_stream_bitrate("front"))
+
+    @patch("frigate.util.live_streams.requests.get")
+    def test_request_error_returns_none(self, mock_get):
+        mock_get.side_effect = requests.ReadTimeout("slow")
+
+        self.assertIsNone(measure_stream_bitrate("front"))
+
+
+if __name__ == "__main__":
+    unittest.main()

frigate/test/test_maintainer.py

@@ -1,7 +1,7 @@
 import datetime
 import sys
 import unittest
-from unittest.mock import MagicMock, patch
+from unittest.mock import AsyncMock, MagicMock, patch
 
 # Mock complex imports before importing maintainer, saving originals so we can
 # restore them after import and avoid polluting sys.modules for other tests.
@@ -16,7 +16,7 @@ for name in _MOCKED_MODULES:
     sys.modules[name] = MagicMock()
 
 # Now import the class under test
-from frigate.config import FrigateConfig  # noqa: E402
+from frigate.config import FrigateConfig, RetainModeEnum  # noqa: E402
 from frigate.record.maintainer import RecordingMaintainer  # noqa: E402
 
 # Restore original modules (or remove mock if there was no original)
@@ -48,8 +48,11 @@ class TestMaintainer(unittest.IsolatedAsyncioTestCase):
                     "frigate.record.maintainer.psutil.process_iter", return_value=[]
                 ):
                     with patch("frigate.record.maintainer.logger.warning") as warn:
-                        # Mock validate_and_move_segment to avoid further logic
+                        # Mock validate_and_move_segment to avoid further logic.
-                        maintainer.validate_and_move_segment = MagicMock()
+                        # The requestor is real when another test imported the
+                        # maintainer first, and it would block on a reply.
+                        maintainer.validate_and_move_segment = AsyncMock()
+                        maintainer.requestor = MagicMock()
 
                         try:
                             await maintainer.move_files()
@@ -121,6 +124,80 @@ class TestMaintainer(unittest.IsolatedAsyncioTestCase):
         self.assertIsNone(result)
         maintainer.drop_segment.assert_called_once_with(cache_path)
 
+    async def test_defers_review_overlap_segment_until_metadata_catches_up(self):
+        # Regression: a segment overlapping an active_objects review must not
+        # be dropped while detection metadata lags behind the segment end,
+        # the missing frames may hold the active objects (or continuous
+        # retention would keep it anyway).
+        config = MagicMock(spec=FrigateConfig)
+
+        camera_config = MagicMock()
+        camera_config.record.enabled = True
+        camera_config.record.continuous.days = 7
+        camera_config.record.motion.days = 0
+        camera_config.record.alerts.retain.mode = RetainModeEnum.active_objects
+        camera_config.record.get_review_pre_capture.return_value = 5
+        camera_config.record.get_review_post_capture.return_value = 5
+        config.cameras = {"test_cam": camera_config}
+
+        stop_event = MagicMock()
+        maintainer = RecordingMaintainer(config, stop_event)
+
+        now = datetime.datetime.now(datetime.UTC)
+        start_time = now - datetime.timedelta(seconds=20)
+        end_time = now - datetime.timedelta(seconds=10)
+        cache_path = "/tmp/cache/test_cam@20260417150000+0000.mp4"
+
+        maintainer.end_time_cache = {
+            cache_path: (end_time, 10.0, False, None, None, None, [])
+        }
+        # Metadata has only reached partway into the segment.
+        maintainer.object_recordings_info["test_cam"] = [
+            (end_time.timestamp() - 8, [], [], [])
+        ]
+        maintainer.audio_recordings_info["test_cam"] = []
+
+        maintainer.drop_segment = MagicMock()
+        maintainer.move_segment = AsyncMock(return_value=None)
+        maintainer.recordings_publisher = MagicMock()
+
+        review = MagicMock()
+        review.severity = "alert"
+        review.start_time = start_time.timestamp() - 30
+        review.end_time = None
+
+        result = await maintainer.validate_and_move_segment(
+            "test_cam",
+            reviews=[review],
+            recording={
+                "start_time": start_time,
+                "cache_path": cache_path,
+                "stream_type": "main",
+            },
+        )
+
+        self.assertIsNone(result)
+        maintainer.drop_segment.assert_not_called()
+        maintainer.move_segment.assert_not_awaited()
+
+        # Once metadata passes the segment end, continuous retention keeps it.
+        maintainer.object_recordings_info["test_cam"].append(
+            (now.timestamp(), [], [], [])
+        )
+
+        await maintainer.validate_and_move_segment(
+            "test_cam",
+            reviews=[review],
+            recording={
+                "start_time": start_time,
+                "cache_path": cache_path,
+                "stream_type": "main",
+            },
+        )
+
+        maintainer.drop_segment.assert_not_called()
+        maintainer.move_segment.assert_awaited_once()
+
     async def test_expire_stale_recordings_info_drops_only_absent_cameras(self):
         config = MagicMock(spec=FrigateConfig)
         config.cameras = {}

frigate/test/test_motion_detector.py

@@ -29,7 +29,6 @@ class TestImprovedMotionDetector(unittest.TestCase):
 
         class DummyPTZ:
             def __init__(self):
-                self.autotracker_enabled = _Stub(False)
                 self.motor_stopped = _Stub(False)
                 self.stop_time = _Stub(0)
 

frigate/test/test_mqtt_lifecycle.py

@@ -126,12 +126,18 @@ class TestMqttClientLifecycle(unittest.TestCase):
             os.makedirs(MODEL_CACHE_DIR)
 
         self.config = build_config()
-        self.client = MqttClient(self.config)
+        self.client = self._build_client()
         self.receiver = RuntimeSnapshotReceiver()
         self.client.attach_dispatcher(build_dispatcher(self.config, []))
 
-    def test_subscribe_stores_receiver_without_starting_worker(self) -> None:
+    def _build_client(self) -> MqttClient:
         client = MqttClient(self.config)
+        self.addCleanup(client._wake_recv.close)
+        self.addCleanup(client._wake_send.close)
+        return client
+
+    def test_subscribe_stores_receiver_without_starting_worker(self) -> None:
+        client = self._build_client()
 
         with patch.object(client, "_start_worker") as mock_start_worker:
             client.subscribe(self.receiver._receive)
@@ -142,7 +148,7 @@ class TestMqttClientLifecycle(unittest.TestCase):
         mock_start_worker.assert_not_called()
 
     def test_attach_dispatcher_supplies_command_surface(self) -> None:
-        client = MqttClient(self.config)
+        client = self._build_client()
 
         self.assertFalse(client._is_supported_command_topic("front/detect/set"))
 
@@ -295,6 +301,13 @@ class TestMqttClientLifecycle(unittest.TestCase):
         self.assertEqual(self.client._subscription_mid, 42)
         self.client.client.subscribe.assert_called_once_with("frigate/#", qos=0)
 
+    def test_publish_wakes_worker(self) -> None:
+        self.client.connected = True
+
+        self.client.publish("events", "payload")
+
+        self.assertEqual(self.client._wake_recv.recv(16), b"\0")
+
     def test_handle_connect_event_reconnects_on_recoverable_subscribe_error(
         self,
     ) -> None:
@@ -448,7 +461,6 @@ class TestMqttClientLifecycle(unittest.TestCase):
 
     def test_publish_direct_waits_for_flush_barrier(self) -> None:
         mock_client = MagicMock()
-        mock_client.loop.return_value = mqtt.MQTT_ERR_SUCCESS
         self.client.client = mock_client
         message_info = MagicMock(rc=mqtt.MQTT_ERR_SUCCESS, mid=1)
         # inflight tracking checks once, then _wait_for_publish polls
@@ -456,11 +468,14 @@ class TestMqttClientLifecycle(unittest.TestCase):
         mock_client.publish.return_value = message_info
         barrier = MagicMock()
 
+        with patch.object(
+            self.client, "_loop_client", return_value=mqtt.MQTT_ERR_SUCCESS
+        ) as mock_loop:
             self.client._publish_direct(
                 QueuedPublish("frigate/available", "stopped", True, barrier)
             )
 
-        mock_client.loop.assert_called_once()
+        mock_loop.assert_called_once()
         barrier.set.assert_called_once()
 
     def test_shutdown_barrier_releases_when_publish_raises(self) -> None:
@@ -643,8 +658,7 @@ class TestMqttClientLifecycle(unittest.TestCase):
             loop_calls[0] += 1
             return mqtt.MQTT_ERR_SUCCESS
 
-        mock_client.loop.side_effect = loop_side_effect
+        with patch.object(self.client, "_loop_client", side_effect=loop_side_effect):
-
             self.client._wait_for_publish(message_info)
 
         self.assertEqual(loop_calls[0], 1)
@@ -669,16 +683,20 @@ class TestMqttClientLifecycle(unittest.TestCase):
 
     def test_mqtt_loop_worker_reconnects_on_recoverable_loop_error(self) -> None:
         self.client.client = MagicMock()
-        self.client.client.loop.side_effect = OSError("socket closed")
 
         def stop_after_reconnect() -> None:
             self.client._stop_event.set()
 
-        with patch.object(
+        with (
+            patch.object(
+                self.client, "_loop_client", side_effect=OSError("socket closed")
+            ),
+            patch.object(
                 self.client,
                 "_schedule_reconnect",
                 side_effect=stop_after_reconnect,
-        ) as mock_schedule_reconnect:
+            ) as mock_schedule_reconnect,
+        ):
             self.client._mqtt_loop_worker()
 
         mock_schedule_reconnect.assert_called_once()

frigate/test/test_mqtt_network_loop.py

@@ -0,0 +1,146 @@
+import fcntl
+import resource
+import selectors
+import socket
+import time
+import unittest
+from unittest.mock import MagicMock, patch
+
+import paho.mqtt.client as mqtt
+from paho.mqtt.enums import CallbackAPIVersion
+
+from frigate.comms.mqtt import MqttClient
+
+
+class TestMqttNetworkLoop(unittest.TestCase):
+    def setUp(self) -> None:
+        self.transport = object.__new__(MqttClient)
+        self.client = MagicMock()
+        self.client.want_write.return_value = False
+        self.client.loop_read.return_value = mqtt.MQTT_ERR_SUCCESS
+        self.client.loop_write.return_value = mqtt.MQTT_ERR_SUCCESS
+        self.client.loop_misc.return_value = mqtt.MQTT_ERR_SUCCESS
+        self.transport.client = self.client
+        self.sock, self.peer = socket.socketpair()
+        self.addCleanup(self.sock.close)
+        self.addCleanup(self.peer.close)
+        self.transport._wake_recv, self.transport._wake_send = socket.socketpair()
+        self.transport._wake_recv.setblocking(False)
+        self.transport._wake_send.setblocking(False)
+        self.addCleanup(self.transport._wake_recv.close)
+        self.addCleanup(self.transport._wake_send.close)
+        self.client.socket.return_value = self.sock
+
+    def test_high_fd_handles_connack_suback_publish_and_puback(self) -> None:
+        """Process real MQTT packets on a socket beyond select()'s FD limit."""
+        if selectors.DefaultSelector is selectors.SelectSelector:
+            self.skipTest("This platform has no selector supporting high socket FDs")
+        original_limit = resource.getrlimit(resource.RLIMIT_NOFILE)
+        soft, hard = original_limit
+        if soft <= 1024:
+            if hard != resource.RLIM_INFINITY and hard <= 1024:
+                self.skipTest("The hard file descriptor limit is too low")
+            new_soft = 2048 if hard == resource.RLIM_INFINITY else min(2048, hard)
+            resource.setrlimit(resource.RLIMIT_NOFILE, (new_soft, hard))
+            self.addCleanup(resource.setrlimit, resource.RLIMIT_NOFILE, original_limit)
+
+        fd = fcntl.fcntl(self.sock.fileno(), fcntl.F_DUPFD, 1024)
+        with socket.socket(fileno=fd) as high_sock:
+            high_sock.setblocking(False)
+            self.peer.settimeout(1)
+            client = mqtt.Client(CallbackAPIVersion.VERSION2, client_id="high-fd-test")
+            client._sock = high_sock
+            self.transport.client = client
+            connected, subscribed, received = [], [], []
+            client.on_connect = lambda *args: connected.append(args[3])
+            client.on_subscribe = lambda *args: subscribed.append(args[2])
+            client.on_message = lambda client, userdata, message: received.append(
+                message.payload
+            )
+
+            self.assertGreaterEqual(high_sock.fileno(), 1024)
+            self.peer.sendall(b"\x20\x02\x00\x00")
+            self.assertEqual(self.transport._loop_client(0.1), mqtt.MQTT_ERR_SUCCESS)
+            self.assertEqual(len(connected), 1)
+            self.assertTrue(client.is_connected())
+
+            result, mid = client.subscribe("diagnostic", qos=1)
+            self.assertEqual(result, mqtt.MQTT_ERR_SUCCESS)
+            self.peer.recv(1024)
+            self.peer.sendall(b"\x90\x03" + mid.to_bytes(2, "big") + b"\x01")
+            self.assertEqual(self.transport._loop_client(0.1), mqtt.MQTT_ERR_SUCCESS)
+            self.assertEqual(subscribed, [mid])
+
+            info = client.publish("diagnostic", b"outgoing", qos=1)
+            self.peer.recv(1024)
+            self.peer.sendall(b"\x40\x02" + info.mid.to_bytes(2, "big"))
+            self.assertEqual(self.transport._loop_client(0.1), mqtt.MQTT_ERR_SUCCESS)
+            self.assertTrue(info.is_published())
+
+            payload = b"\x00\x0adiagnosticincoming"
+            self.peer.sendall(b"\x30" + bytes([len(payload)]) + payload)
+            self.assertEqual(self.transport._loop_client(0.1), mqtt.MQTT_ERR_SUCCESS)
+            self.assertEqual(received, [b"incoming"])
+
+    def test_idle_socket_still_runs_keepalive(self) -> None:
+        self.assertEqual(self.transport._loop_client(0), mqtt.MQTT_ERR_SUCCESS)
+        self.client.loop_read.assert_not_called()
+        self.client.loop_write.assert_not_called()
+        self.client.loop_misc.assert_called_once()
+
+    def test_writable_socket_flushes_pending_packets(self) -> None:
+        self.client.want_write.return_value = True
+        self.assertEqual(self.transport._loop_client(0), mqtt.MQTT_ERR_SUCCESS)
+        self.client.loop_write.assert_called_once()
+        self.client.loop_read.assert_not_called()
+
+    def test_tls_buffer_is_read_without_waiting_for_socket_readiness(self) -> None:
+        tls_sock = MagicMock()
+        tls_sock.fileno.return_value = self.sock.fileno()
+        tls_sock.pending.return_value = 1
+        self.client.socket.return_value = tls_sock
+        with patch("frigate.comms.mqtt.selectors.DefaultSelector") as selector:
+            selector.return_value.__enter__.return_value.select.return_value = []
+            self.assertEqual(self.transport._loop_client(1), mqtt.MQTT_ERR_SUCCESS)
+            selector.return_value.__enter__.return_value.select.assert_called_once_with(
+                0.0
+            )
+        self.client.loop_read.assert_called_once()
+
+    def test_read_failure_does_not_write_or_run_keepalive(self) -> None:
+        self.peer.sendall(b"ready")
+        self.client.want_write.return_value = True
+        self.client.loop_read.return_value = mqtt.MQTT_ERR_CONN_LOST
+        self.assertEqual(self.transport._loop_client(0), mqtt.MQTT_ERR_CONN_LOST)
+        self.client.loop_write.assert_not_called()
+        self.client.loop_misc.assert_not_called()
+
+    def test_socket_closed_during_read_does_not_write(self) -> None:
+        self.peer.sendall(b"ready")
+        self.client.want_write.return_value = True
+        self.client.socket.side_effect = [self.sock, None]
+        self.transport._loop_client(0)
+        self.client.loop_write.assert_not_called()
+        self.client.loop_misc.assert_not_called()
+
+    def test_write_failure_does_not_run_keepalive(self) -> None:
+        self.client.want_write.return_value = True
+        self.client.loop_write.return_value = mqtt.MQTT_ERR_CONN_LOST
+        self.assertEqual(self.transport._loop_client(0), mqtt.MQTT_ERR_CONN_LOST)
+        self.client.loop_misc.assert_not_called()
+
+    def test_missing_socket_reports_no_connection(self) -> None:
+        self.client.socket.return_value = None
+        self.assertEqual(self.transport._loop_client(0), mqtt.MQTT_ERR_NO_CONN)
+
+    def test_wake_interrupts_wait_and_is_consumed(self) -> None:
+        self.transport._wake_worker()
+
+        start = time.monotonic()
+        self.assertEqual(self.transport._loop_client(5), mqtt.MQTT_ERR_SUCCESS)
+        self.assertLess(time.monotonic() - start, 1)
+        self.client.loop_read.assert_not_called()
+
+        start = time.monotonic()
+        self.transport._loop_client(0.2)
+        self.assertGreater(time.monotonic() - start, 0.15)

frigate/test/test_ptz_autotrack.py

@@ -7,9 +7,8 @@ KeyError on the autotracker thread or silently keep the wrong state:
 
 - autotracker_init only got an entry for cameras enabled when PtzAutoTracker was
   constructed, so runtime-enabled cameras raised KeyError on lookup.
-- ptz_metrics autotracker_enabled is what the camera processes read, but nothing
+- _disable only changed the main process config, so the camera process kept
-  updated it when autotracking was enabled through a config save, so it stayed
+  running its motion estimator for a camera that could not autotrack.
-  False and the tracker never built a motion estimator.
 """
 
 import unittest
@@ -17,7 +16,8 @@ from unittest.mock import MagicMock
 
 from frigate.camera import PTZMetrics
 from frigate.config import FrigateConfig
-from frigate.ptz.autotrack import PtzAutoTracker
+from frigate.config.camera.updater import CameraConfigUpdateEnum
+from frigate.ptz.autotrack import PtzAutoTracker, calculate_max_target_box
 
 CAMERA = "ptz_cam"
 
@@ -53,8 +53,9 @@ def _make_tracker(autotracking_enabled: bool = True) -> PtzAutoTracker:
     onvif over the network. Only the config/metrics state is relevant here."""
     tracker = PtzAutoTracker.__new__(PtzAutoTracker)
     tracker.config = _config(autotracking_enabled)
-    tracker.ptz_metrics = {CAMERA: PTZMetrics(autotracker_enabled=False)}
+    tracker.ptz_metrics = {CAMERA: PTZMetrics()}
     tracker.onvif = MagicMock()
+    tracker.dispatcher = MagicMock()
     tracker.config_subscriber = MagicMock()
     tracker.autotracker_init = {}
     tracker.calibrating = {}
@@ -83,47 +84,49 @@ class TestAutotrackerInitGuards(unittest.IsolatedAsyncioTestCase):
         tracker.onvif.get_camera_status.assert_not_called()
 
 
-class TestAutotrackerMetricSync(unittest.TestCase):
+class TestAutotrackerEnqueueMove(unittest.TestCase):
-    def test_metric_follows_config_when_enabled_by_update(self) -> None:
+    def _enqueue(self, pan: float, tilt: float, zoom: float) -> MagicMock:
-        # autotracking enabled via a config save: the metric was seeded False when
+        tracker = _make_tracker()
-        # the camera was added and nothing else updates it
+        tracker.move_queues = {CAMERA: MagicMock()}
+        tracker.move_queue_locks = {CAMERA: MagicMock()}
+        tracker.move_queue_locks[CAMERA].locked.return_value = False
+
+        tracker._enqueue_move(CAMERA, 1000.0, pan, tilt, zoom)
+
+        return tracker.onvif.loop.call_soon_threadsafe
+
+    def test_move_is_clipped_to_the_onvif_range(self) -> None:
+        # velocity estimates can push the predicted centroid outside the frame
+        call_soon = self._enqueue(1.7, -2.5, 0.4)
+
+        call_soon.assert_called_once()
+        self.assertEqual(call_soon.call_args.args[1], (1000.0, 1.0, -1.0, 0.4))
+
+    def test_empty_move_is_not_enqueued(self) -> None:
+        self._enqueue(0, 0, 0).assert_not_called()
+
+
+class TestAutotrackerDisable(unittest.TestCase):
+    def test_disable_publishes_to_camera_process(self) -> None:
         tracker = _make_tracker(autotracking_enabled=True)
-        metrics = tracker.ptz_metrics[CAMERA]
-        self.assertFalse(metrics.autotracker_enabled.value)
 
-        tracker.config_subscriber.check_for_updates.return_value = {"onvif": [CAMERA]}
+        tracker._disable(CAMERA, "onvif connection failed")
-        tracker.check_for_updates()
 
-        self.assertTrue(metrics.autotracker_enabled.value)
+        autotracking = tracker.config.cameras[CAMERA].onvif.autotracking
+        self.assertFalse(autotracking.enabled)
 
-    def test_metric_follows_config_when_disabled_by_update(self) -> None:
+        publish = tracker.dispatcher.config_updater.publish_update
-        tracker = _make_tracker(autotracking_enabled=False)
+        publish.assert_called_once()
-        metrics = tracker.ptz_metrics[CAMERA]
+        topic, payload = publish.call_args.args
-        metrics.autotracker_enabled.value = True
+        self.assertEqual(topic.update_type, CameraConfigUpdateEnum.autotracking)
+        self.assertEqual(topic.camera, CAMERA)
+        self.assertIs(payload, autotracking)
 
-        tracker.config_subscriber.check_for_updates.return_value = {
-            "autotracking": [CAMERA]
-        }
-        tracker.check_for_updates()
 
-        self.assertFalse(metrics.autotracker_enabled.value)
+class TestMaxTargetBox(unittest.TestCase):
-
+    def test_follows_zoom_factor(self) -> None:
-    def test_metric_sync_skips_camera_without_metrics(self) -> None:
+        self.assertAlmostEqual(calculate_max_target_box(0.5), 0.6**2)
-        # `add` reaches the maintainer and the autotracker on separate threads with
+        self.assertAlmostEqual(calculate_max_target_box(0.25), 0.6**4)
-        # no ordering guarantee, so the metrics may not exist yet
-        tracker = _make_tracker()
-        tracker.ptz_metrics = {}
-        tracker.config_subscriber.check_for_updates.return_value = {"add": [CAMERA]}
-
-        tracker.check_for_updates()
-
-    def test_metric_sync_skips_unknown_camera(self) -> None:
-        tracker = _make_tracker()
-        tracker.config_subscriber.check_for_updates.return_value = {
-            "add": ["not_in_config"]
-        }
-
-        tracker.check_for_updates()
 
 
 if __name__ == "__main__":

frigate/test/test_ptz_onvif.py

@@ -2,14 +2,9 @@
 
 Regression coverage for a camera that is initialized while autotracking is off and
 has it enabled later, which is the normal wizard flow: set the camera up first,
-configure autotracking afterwards. The autotracking-only request objects used to
+configure autotracking afterwards. get_camera_status skips its re-init branch when
-be created only when autotracking was enabled at init time, so the camera was left
+init is True, so everything it reads must exist whether or not autotracking was
-with init=True but no status_request. get_camera_status skips its re-init branch
+enabled at init time.
-when init is True, so it went straight to the missing key and raised KeyError on
-the tracking thread.
-
-The request objects are built from the locally parsed WSDL and cost no network, so
-they are always created and init=True now implies they exist.
 
 Also covers the inverse direction: the ptz movement timestamps must not be written
 for a camera that has autotracking off, because nothing clears them back out.
@@ -99,7 +94,6 @@ def _make_controller(autotracking_enabled: bool) -> OnvifController:
     controller.config = config
     controller.cams = {CAMERA: {"onvif": _make_onvif_camera(), "init": False}}
     controller.failed_cams = {}
-    controller.camera_configs = {CAMERA: config.cameras[CAMERA]}
     controller.ptz_metrics = {CAMERA: MagicMock()}
     return controller
 
@@ -110,7 +104,6 @@ def _make_move_controller(autotracking_enabled: bool) -> OnvifController:
     config = _config(autotracking_enabled)
     controller = OnvifController.__new__(OnvifController)
     controller.config = config
-    controller.camera_configs = {CAMERA: config.cameras[CAMERA]}
     controller.failed_cams = {}
 
     ptz = MagicMock()
@@ -128,45 +121,30 @@ def _make_move_controller(autotracking_enabled: bool) -> OnvifController:
             },
         }
     }
-    controller.ptz_metrics = {
+    controller.ptz_metrics = {CAMERA: PTZMetrics()}
-        CAMERA: PTZMetrics(autotracker_enabled=autotracking_enabled)
-    }
     return controller
 
 
 class TestOnvifInitRequests(unittest.IsolatedAsyncioTestCase):
-    async def test_status_request_created_when_autotracking_disabled(self) -> None:
+    async def test_camera_status_independent_of_autotracking_at_init(self) -> None:
         # the wizard flow: onvif configured first, autotracking enabled later
-        controller = _make_controller(autotracking_enabled=False)
-
-        self.assertTrue(await controller._init_onvif(CAMERA))
-
-        cam = controller.cams[CAMERA]
-        self.assertTrue(cam["init"])
-        self.assertIn("status_request", cam)
-        self.assertIn("service_capabilities_request", cam)
-
-    async def test_status_request_created_when_autotracking_enabled(self) -> None:
-        controller = _make_controller(autotracking_enabled=True)
-
-        self.assertTrue(await controller._init_onvif(CAMERA))
-
-        cam = controller.cams[CAMERA]
-        self.assertIn("status_request", cam)
-        self.assertIn("service_capabilities_request", cam)
-
-    async def test_init_implies_status_request_exists(self) -> None:
-        # the invariant get_camera_status relies on: it skips re-init when init is
-        # True and then reads status_request without guarding
         for autotracking_enabled in (True, False):
             with self.subTest(autotracking_enabled=autotracking_enabled):
                 controller = _make_controller(autotracking_enabled)
+                controller.status_locks = {CAMERA: asyncio.Lock()}
 
-                await controller._init_onvif(CAMERA)
+                self.assertTrue(await controller._init_onvif(CAMERA))
 
-                cam = controller.cams[CAMERA]
+                status = MagicMock()
-                if cam["init"]:
+                status.MoveStatus.PanTilt = "IDLE"
-                    self.assertEqual(cam["status_request"].request_type, "GetStatus")
+                status.MoveStatus.Zoom = "IDLE"
+                ptz = controller.cams[CAMERA]["ptz"]
+                ptz.GetStatus = AsyncMock(return_value=status)
+
+                await controller.get_camera_status(CAMERA)
+
+                ptz.GetStatus.assert_awaited_once_with({"ProfileToken": "profile_1"})
+                self.assertFalse(controller.cams[CAMERA]["active"])
 
     async def test_requests_built_without_contacting_camera(self) -> None:
         # create_type is a local WSDL lookup; cameras that do not implement

frigate/test/test_record_sub_maintainer.py

@@ -1,5 +1,6 @@
 """Tests for sub stream cache segment handling in the recording maintainer."""
 
+import asyncio
 import datetime
 import os
 import tempfile
@@ -532,6 +533,59 @@ class TestSegmentStartChaining(unittest.IsolatedAsyncioTestCase):
         self.assertAlmostEqual(calls[1].args[2].timestamp(), self.T0 + 10.4, places=3)
         self.assertAlmostEqual(calls[1].args[3].timestamp(), self.T0 + 20.8, places=3)
 
+    async def test_out_of_order_probes_chain_in_segment_order(self):
+        maintainer = _build_chaining_maintainer(self.T0)
+
+        async def probe(_ffmpeg, cache_path, get_duration=False):
+            # the earlier segment's probe finishes last
+            if "chain0" in cache_path:
+                await asyncio.sleep(0.05)
+
+            return {"has_valid_video": True, "duration": 10.4}
+
+        recordings = [
+            {
+                "start_time": datetime.datetime.fromtimestamp(
+                    self.T0 + offset, tz=datetime.UTC
+                ),
+                "cache_path": f"/tmp/cache/test_cam@chain{offset}.mp4",
+                "stream_type": "main",
+            }
+            for offset in (0, 10)
+        ]
+        first_resolved = asyncio.Event()
+
+        with (
+            patch("frigate.record.maintainer.get_video_properties", probe),
+            patch(
+                "frigate.record.maintainer.get_keyframe_offsets",
+                AsyncMock(return_value=[0]),
+            ),
+            patch(
+                "frigate.record.maintainer.os.path.getmtime",
+                MagicMock(side_effect=OSError("missing")),
+            ),
+        ):
+            await asyncio.gather(
+                maintainer._validate_in_order(
+                    "test_cam", [], recordings[0], None, first_resolved
+                ),
+                maintainer._validate_in_order(
+                    "test_cam", [], recordings[1], first_resolved, asyncio.Event()
+                ),
+            )
+
+        starts = sorted(
+            call.args[2].timestamp() for call in maintainer.move_segment.await_args_list
+        )
+        self.assertEqual(starts[0], self.T0)
+        self.assertAlmostEqual(starts[1], self.T0 + 10.4, places=3)
+        self.assertAlmostEqual(
+            maintainer.last_segment_end[("test_cam", "main")],
+            self.T0 + 20.8,
+            places=3,
+        )
+
     async def test_genuine_gap_is_not_snapped(self):
         maintainer = _build_chaining_maintainer(self.T0)
 

frigate/test/test_review_annotations.py

@@ -1,10 +1,13 @@
 """Tests for tracker-derived review frame annotations."""
 
 import unittest
+from unittest.mock import patch
 
 from frigate.data_processing.post.review_annotations import (
     annotations_by_frame,
+    build_frame_captions,
     build_timeline,
+    describe_classification_change,
     describe_heading,
     describe_position,
     event_name,
@@ -312,5 +315,75 @@ class TestFrameBucketing(unittest.TestCase):
         self.assertEqual(annotations_by_frame([(1.0, "x")], []), {})
 
 
+class TestClassificationChangeCaptions(unittest.TestCase):
+    def setUp(self):
+        person = track(
+            "1789481994.684479-lpyc2z",
+            "person",
+            0.0,
+            straight_path((0.9, 0.6), (0.4, 0.3), 10, 0.0),
+        )
+        patcher = patch(
+            "frigate.data_processing.post.review_annotations.get_tracked_events",
+            return_value=[person],
+        )
+        patcher.start()
+        self.addCleanup(patcher.stop)
+        patcher = patch(
+            "frigate.data_processing.post.review_annotations.get_state_changes",
+            return_value=[],
+        )
+        patcher.start()
+        self.addCleanup(patcher.stop)
+
+    def test_change_is_phrased_without_underscores(self):
+        self.assertEqual(
+            describe_classification_change(
+                {"model": "trash_day", "from": "no_bins", "to": "bins_at_curb"}
+            ),
+            "trash day changed from no bins to bins at curb",
+        )
+
+    def test_change_is_noted_before_the_frame_it_precedes(self):
+        change = {"model": "front_gate", "from": "closed", "to": "open"}
+        captions = build_frame_captions(
+            ["1789481994.684479-lpyc2z"],
+            [0.0, 10.0, 20.0],
+            [{**change, "timestamp": 12.0}],
+        )
+        self.assertEqual(
+            captions[2],
+            "Frame 3 of 3 (+20.0s):\n[state] front gate changed from closed to open",
+        )
+        self.assertTrue(captions[0].splitlines()[1].startswith("[tracker] "))
+
+    def test_change_is_noted_without_tracked_objects(self):
+        with patch(
+            "frigate.data_processing.post.review_annotations.get_tracked_events",
+            return_value=[],
+        ):
+            captions = build_frame_captions(
+                [],
+                [0.0, 10.0],
+                [{"model": "gate", "from": "a", "to": "b", "timestamp": 5.0}],
+            )
+
+        self.assertEqual(
+            captions,
+            [
+                "Frame 1 of 2 (+0.0s):",
+                "Frame 2 of 2 (+10.0s):\n[state] gate changed from a to b",
+            ],
+        )
+
+    def test_change_after_the_last_frame_is_dropped(self):
+        captions = build_frame_captions(
+            ["1789481994.684479-lpyc2z"],
+            [0.0, 10.0, 20.0],
+            [{"model": "gate", "from": "a", "to": "b", "timestamp": 25.0}],
+        )
+        self.assertFalse(any("[state]" in caption for caption in captions))
+
+
 if __name__ == "__main__":
     unittest.main()

frigate/test/test_review_classification_state.py

@@ -0,0 +1,258 @@
+"""Tests for attaching state classification changes to review items."""
+
+import unittest
+from unittest.mock import MagicMock, patch
+
+from frigate.comms.embeddings_updater import EmbeddingsRequestEnum
+from frigate.config import FrigateConfig
+from frigate.data_processing.post.review_descriptions import (
+    ReviewDescriptionProcessor,
+    format_classification_state_changes,
+)
+from frigate.data_processing.real_time.custom_classification import (
+    CustomStateClassificationProcessor,
+)
+from frigate.models import ReviewSegment
+from frigate.review.maintainer import (
+    CLASSIFICATION_STATE_PRE_ROLL,
+    PendingReviewSegment,
+    ReviewSegmentMaintainer,
+)
+from frigate.review.types import SeverityEnum
+
+CONFIG = """
+mqtt:
+  enabled: False
+cameras:
+  front_door:
+    ffmpeg:
+      inputs:
+        - path: rtsp://10.0.0.1:554/video
+          roles:
+            - detect
+    detect:
+      width: 1920
+      height: 1080
+      fps: 5
+"""
+
+
+def gate_change(timestamp: float, before: str = "closed", after: str = "open"):
+    return {"model": "front_gate", "from": before, "to": after, "timestamp": timestamp}
+
+
+class TestVerifyStateChange(unittest.TestCase):
+    def setUp(self):
+        self.processor = CustomStateClassificationProcessor.__new__(
+            CustomStateClassificationProcessor
+        )
+        self.processor.state_history = {}
+
+    def verify(self, state: str, timestamp: float):
+        return self.processor.verify_state_change("front_door", state, timestamp)
+
+    def test_first_verified_state_has_no_previous_state(self):
+        self.assertIsNone(self.verify("closed", 1.0))
+        self.assertIsNone(self.verify("closed", 2.0))
+        self.assertEqual(self.verify("closed", 3.0), (None, 1.0))
+
+    def test_change_reports_previous_state_and_first_sighting(self):
+        for timestamp in (1.0, 2.0, 3.0):
+            self.verify("closed", timestamp)
+
+        self.assertIsNone(self.verify("open", 10.0))
+        self.assertIsNone(self.verify("open", 11.0))
+        self.assertEqual(self.verify("open", 12.0), ("closed", 10.0))
+
+    def test_interrupted_verification_restarts_the_first_sighting(self):
+        for timestamp in (1.0, 2.0, 3.0):
+            self.verify("closed", timestamp)
+
+        self.verify("open", 10.0)
+        self.verify("closed", 11.0)
+        self.verify("open", 20.0)
+        self.verify("open", 21.0)
+        self.assertEqual(self.verify("open", 22.0), ("closed", 20.0))
+
+    def test_reload_forgets_states_the_model_no_longer_has(self):
+        for timestamp in (1.0, 2.0, 3.0):
+            self.verify("closed", timestamp)
+
+        self.processor.state_history["back_door"] = {"current_state": "open"}
+        self.processor.labelmap = {0: "open", 1: "shut"}
+        self.processor._forget_unknown_states()
+
+        self.assertEqual(list(self.processor.state_history), ["back_door"])
+        self.assertIsNone(self.verify("shut", 10.0))
+        self.assertIsNone(self.verify("shut", 11.0))
+        self.assertEqual(self.verify("shut", 12.0), (None, 10.0))
+
+
+class TestReviewSegmentAttachment(unittest.TestCase):
+    def setUp(self):
+        self.maintainer = ReviewSegmentMaintainer.__new__(ReviewSegmentMaintainer)
+        self.maintainer.config = FrigateConfig.parse_yaml(CONFIG)
+        self.maintainer.active_review_segments = {}
+        self.maintainer.recent_classification_state_changes = {}
+        self.maintainer._publish_segment_update = MagicMock()
+
+    def segment(self, start_time: float) -> PendingReviewSegment:
+        return PendingReviewSegment(
+            "front_door",
+            start_time,
+            SeverityEnum.alert,
+            {"1.0-abcdef": "person"},
+            {},
+            [],
+            set(),
+        )
+
+    def test_change_during_segment_is_attached_and_published(self):
+        segment = self.segment(100.0)
+        self.maintainer.active_review_segments["front_door"] = segment
+
+        self.maintainer.handle_classification_state_change(
+            "front_door", gate_change(110.0)
+        )
+
+        self.assertEqual(segment.classification_state_changes, [gate_change(110.0)])
+        self.maintainer._publish_segment_update.assert_called_once()
+        prev_data = self.maintainer._publish_segment_update.call_args.args[4]
+        self.assertEqual(prev_data["data"]["classification_state_changes"], [])
+        self.assertEqual(
+            segment.get_data(False)["data"]["classification_state_changes"],
+            [gate_change(110.0)],
+        )
+
+    def test_change_never_starts_a_segment(self):
+        self.maintainer.handle_classification_state_change(
+            "front_door", gate_change(110.0)
+        )
+
+        self.assertIsNone(self.maintainer.active_review_segments.get("front_door"))
+        self.maintainer._publish_segment_update.assert_not_called()
+
+    def test_change_just_before_a_segment_is_attached_when_it_starts(self):
+        self.maintainer.handle_classification_state_change(
+            "front_door", gate_change(98.0)
+        )
+        segment = self.segment(100.0)
+
+        self.maintainer._activate_segment(segment)
+
+        self.assertIs(self.maintainer.active_review_segments["front_door"], segment)
+        self.assertEqual(segment.classification_state_changes, [gate_change(98.0)])
+        self.assertNotIn(
+            "front_door", self.maintainer.recent_classification_state_changes
+        )
+
+    def test_change_long_before_a_segment_is_not_attached(self):
+        self.maintainer.handle_classification_state_change(
+            "front_door", gate_change(100.0 - CLASSIFICATION_STATE_PRE_ROLL - 1)
+        )
+        segment = self.segment(100.0)
+
+        self.maintainer._activate_segment(segment)
+
+        self.assertEqual(segment.classification_state_changes, [])
+
+    def test_held_changes_are_pruned(self):
+        self.maintainer.handle_classification_state_change(
+            "front_door", gate_change(10.0)
+        )
+        self.maintainer.handle_classification_state_change(
+            "front_door", gate_change(50.0, "open", "closed")
+        )
+
+        self.assertEqual(
+            self.maintainer.recent_classification_state_changes["front_door"],
+            [gate_change(50.0, "open", "closed")],
+        )
+
+
+class TestChangeTiming(unittest.TestCase):
+    def test_changes_are_placed_relative_to_the_activity(self):
+        lines = format_classification_state_changes(
+            [
+                gate_change(98.0),
+                gate_change(112.4, "open", "closed"),
+                gate_change(140.0),
+            ],
+            start_time=100.0,
+            end_time=130.0,
+        )
+
+        self.assertEqual(
+            lines,
+            [
+                "front gate changed from closed to open, "
+                "just before the activity started",
+                "front gate changed from open to closed, 12s into the activity",
+                "front gate changed from closed to open, after the activity ended",
+            ],
+        )
+
+
+class TestSummaryContext(unittest.TestCase):
+    def row(self, camera, start, end, threat, changes=()):
+        return {
+            "camera": camera,
+            "start_time": start,
+            "end_time": end,
+            "data": {
+                "metadata": {"title": camera, "potential_threat_level": threat},
+                "classification_state_changes": list(changes),
+            },
+        }
+
+    def summarize(self, rows):
+        processor = ReviewDescriptionProcessor.__new__(ReviewDescriptionProcessor)
+        processor.config = FrigateConfig.parse_yaml(CONFIG)
+        processor.genai_manager = MagicMock()
+        client = processor.genai_manager.description_client
+
+        with patch.object(ReviewSegment, "select") as select:
+            query = select.return_value.where.return_value.order_by.return_value
+            query.dicts.return_value.iterator.return_value = iter(rows)
+            processor.handle_request(
+                EmbeddingsRequestEnum.summarize_review.value,
+                {"start_ts": 0, "end_ts": 100},
+            )
+
+        return client.generate_review_summary.call_args.args[2]
+
+    def test_context_state_changes_stay_with_their_review(self):
+        events = self.summarize(
+            [
+                self.row("front_door", 10, 60, 1),
+                self.row("driveway", 15, 25, 0, [gate_change(20.0)]),
+                self.row("driveway", 30, 40, 0, [gate_change(35.0, "open", "closed")]),
+            ]
+        )
+
+        self.assertEqual(
+            [
+                (item["start_time"], item["end_time"], item["state_changes"])
+                for item in events[0]["context"]
+            ],
+            [
+                (15, 25, ["front gate changed from closed to open"]),
+                (30, 40, ["front gate changed from open to closed"]),
+            ],
+        )
+
+    def test_later_context_review_without_changes_is_deduplicated(self):
+        events = self.summarize(
+            [
+                self.row("front_door", 10, 60, 1),
+                self.row("driveway", 15, 25, 0, [gate_change(20.0)]),
+                self.row("driveway", 30, 40, 0),
+            ]
+        )
+
+        self.assertEqual(len(events[0]["context"]), 1)
+        self.assertEqual(events[0]["context"][0]["start_time"], 15)
+
+
+if __name__ == "__main__":
+    unittest.main()

frigate/test/test_review_flow.py

@@ -0,0 +1,857 @@
+"""Tests for how tracked objects flow into review segments.
+
+Frames are fed through the maintainer's run loop with mocked subscribers so
+the dispatch between starting and updating segments is exercised, and the
+published review updates are checked for the expected alert, detection, or
+lack of a review item.
+"""
+
+import json
+import tempfile
+import threading
+import unittest
+from pathlib import Path
+from typing import Any
+from unittest.mock import MagicMock, patch
+
+import numpy as np
+
+from frigate.comms.detections_updater import DetectionTypeEnum
+from frigate.config import FrigateConfig
+from frigate.review.maintainer import ReviewSegmentMaintainer
+
+CAMERA = "front_door"
+
+BASE_CONFIG = """
+mqtt:
+  enabled: False
+record:
+  enabled: True
+cameras:
+  front_door:
+    ffmpeg:
+      inputs:
+        - path: rtsp://10.0.0.1:554/video
+          roles:
+            - detect
+    detect:
+      width: 640
+      height: 360
+      fps: 5
+    zones:
+      driveway:
+        coordinates: 0,0,320,0,320,360,0,360
+      yard:
+        coordinates: 320,0,640,0,640,360,320,360
+%s
+"""
+
+
+class ReviewFlowTestCase(unittest.TestCase):
+    review_config = ""
+
+    def setUp(self) -> None:
+        self.clips_dir = clips_dir = tempfile.TemporaryDirectory()
+        self.addCleanup(clips_dir.cleanup)
+        clips_patch = patch("frigate.review.maintainer.CLIPS_DIR", clips_dir.name)
+        clips_patch.start()
+        self.addCleanup(clips_patch.stop)
+
+        self.maintainer = self._make_maintainer(self.review_config)
+
+    def _make_maintainer(self, review_config: str) -> ReviewSegmentMaintainer:
+        """Build a maintainer without invoking __init__ (avoids needing ZMQ
+        sockets and shared memory)."""
+        maintainer = ReviewSegmentMaintainer.__new__(ReviewSegmentMaintainer)
+        threading.Thread.__init__(maintainer)
+        maintainer.config = FrigateConfig.parse_yaml(BASE_CONFIG % review_config)
+        maintainer.active_review_segments = {}
+        maintainer.indefinite_events = {}
+        maintainer.recent_classification_state_changes = {}
+        maintainer.requestor = MagicMock()
+        maintainer.review_publisher = MagicMock()
+        maintainer.config_subscriber = MagicMock()
+        maintainer.config_subscriber.check_for_updates.return_value = {}
+        maintainer.detection_subscriber = MagicMock()
+        maintainer.frame_manager = MagicMock()
+        maintainer.frame_manager.get.side_effect = lambda _name, shape: np.zeros(
+            shape, np.uint8
+        )
+        return maintainer
+
+    @property
+    def stationary_threshold(self) -> int:
+        return self.maintainer.config.cameras[CAMERA].detect.stationary.threshold
+
+    @property
+    def alert_cutoff(self) -> int:
+        return self.maintainer.config.cameras[CAMERA].review.alerts.cutoff_time
+
+    @property
+    def detection_cutoff(self) -> int:
+        return self.maintainer.config.cameras[CAMERA].review.detections.cutoff_time
+
+    def tracked(
+        self,
+        obj_id: str,
+        label: str,
+        frame_time: float,
+        *,
+        start_time: float = 0,
+        zones: list[str] | None = None,
+        stationary: bool = False,
+        loitering: bool = False,
+        moved: bool = True,
+        false_positive: bool = False,
+        sub_label: tuple[str, float] | None = None,
+    ) -> dict[str, Any]:
+        """Build a tracked object as published by the object processor."""
+        return {
+            "id": obj_id,
+            "label": label,
+            "sub_label": sub_label,
+            "frame_time": frame_time,
+            "start_time": start_time,
+            "motionless_count": self.stationary_threshold if stationary else 0,
+            "pending_loitering": loitering,
+            "position_changes": 1 if moved else 0,
+            "false_positive": false_positive,
+            "current_zones": zones or [],
+            "box": (100, 100, 200, 200),
+        }
+
+    def feed(self, *frames: tuple[float, list[dict[str, Any]]]) -> None:
+        """Run the maintainer loop over the given (frame_time, objects) frames."""
+        queue = [
+            (
+                DetectionTypeEnum.video.value,
+                (CAMERA, f"{CAMERA}_{frame_time}", frame_time, objects, [], []),
+            )
+            for frame_time, objects in frames
+        ]
+        self.maintainer.stop_event = threading.Event()
+
+        def next_update(timeout: float) -> Any:
+            if not queue:
+                self.maintainer.stop_event.set()
+                return None
+
+            return queue.pop(0)
+
+        self.maintainer.detection_subscriber.check_for_update.side_effect = next_update
+        self.maintainer.run()
+
+    def reviews(self) -> list[dict[str, Any]]:
+        """All review updates published on the reviews topic."""
+        return [
+            json.loads(c.args[1])
+            for c in self.maintainer.requestor.send_data.call_args_list
+            if c.args[0] == "reviews"
+        ]
+
+    def review_summary(self) -> list[tuple[str, str]]:
+        return [(r["type"], r["after"]["severity"]) for r in self.reviews()]
+
+    def non_update_summary(self) -> list[tuple[str, str]]:
+        return [(t, s) for t, s in self.review_summary() if t != "update"]
+
+    def thumbnails(self) -> set[str]:
+        """Names of the review thumbnails on disk."""
+        return {t.name for t in Path(self.clips_dir.name, "review").iterdir()}
+
+    def assert_no_review(self) -> None:
+        self.assertEqual(self.reviews(), [])
+        self.assertIsNone(self.maintainer.active_review_segments.get(CAMERA))
+
+
+class TestReviewSeverity(ReviewFlowTestCase):
+    def test_alert_label_creates_alert(self) -> None:
+        self.feed((1, [self.tracked("p1", "person", 1)]))
+
+        self.assertEqual(self.review_summary(), [("new", "alert")])
+        self.assertEqual(self.reviews()[0]["after"]["data"]["objects"], ["person"])
+
+    def test_non_alert_label_creates_detection(self) -> None:
+        self.feed((1, [self.tracked("d1", "dog", 1)]))
+
+        self.assertEqual(self.review_summary(), [("new", "detection")])
+        self.assertEqual(self.reviews()[0]["after"]["data"]["objects"], ["dog"])
+
+    def test_alert_and_detection_objects_create_single_alert(self) -> None:
+        self.feed((1, [self.tracked("p1", "person", 1), self.tracked("d1", "dog", 1)]))
+
+        self.assertEqual(self.review_summary(), [("new", "alert")])
+        self.assertCountEqual(
+            self.reviews()[0]["after"]["data"]["objects"], ["person", "dog"]
+        )
+
+    def test_no_objects_creates_nothing(self) -> None:
+        self.feed((1, []), (2, []))
+
+        self.assert_no_review()
+
+
+class TestIgnoredObjects(ReviewFlowTestCase):
+    def test_stationary_object_creates_nothing(self) -> None:
+        self.feed((1, [self.tracked("p1", "person", 1, stationary=True)]))
+
+        self.assert_no_review()
+
+    def test_stationary_loitering_object_creates_alert(self) -> None:
+        self.feed(
+            (1, [self.tracked("p1", "person", 1, stationary=True, loitering=True)])
+        )
+
+        self.assertEqual(self.review_summary(), [("new", "alert")])
+
+    def test_object_that_never_moved_creates_nothing(self) -> None:
+        self.feed((1, [self.tracked("p1", "person", 1, moved=False)]))
+
+        self.assert_no_review()
+
+    def test_object_not_detected_in_current_frame_creates_nothing(self) -> None:
+        self.feed((2, [self.tracked("p1", "person", 1)]))
+
+        self.assert_no_review()
+
+    def test_false_positive_creates_nothing(self) -> None:
+        self.feed((1, [self.tracked("p1", "person", 1, false_positive=True)]))
+
+        self.assert_no_review()
+
+
+class TestAlertRequiredZones(ReviewFlowTestCase):
+    review_config = """
+    review:
+      alerts:
+        required_zones: driveway
+"""
+
+    def test_alert_label_in_required_zone_creates_alert(self) -> None:
+        self.feed((1, [self.tracked("p1", "person", 1, zones=["driveway"])]))
+
+        self.assertEqual(self.review_summary(), [("new", "alert")])
+        self.assertEqual(self.reviews()[0]["after"]["data"]["zones"], ["driveway"])
+
+    def test_alert_label_outside_required_zone_creates_detection(self) -> None:
+        self.feed((1, [self.tracked("p1", "person", 1, zones=["yard"])]))
+
+        self.assertEqual(self.review_summary(), [("new", "detection")])
+
+    def test_alert_label_in_no_zone_creates_detection(self) -> None:
+        self.feed((1, [self.tracked("p1", "person", 1)]))
+
+        self.assertEqual(self.review_summary(), [("new", "detection")])
+
+    def test_detection_upgrades_to_alert_when_object_enters_required_zone(
+        self,
+    ) -> None:
+        self.feed(
+            (1, [self.tracked("p1", "person", 1, zones=["yard"])]),
+            (2, [self.tracked("p1", "person", 2, zones=["driveway"])]),
+        )
+
+        self.assertEqual(
+            self.review_summary(), [("new", "detection"), ("update", "alert")]
+        )
+        self.assertEqual(
+            self.reviews()[0]["after"]["id"], self.reviews()[1]["after"]["id"]
+        )
+
+
+class TestDetectionRequiredZones(ReviewFlowTestCase):
+    review_config = """
+    review:
+      detections:
+        required_zones: yard
+"""
+
+    def test_detection_label_in_required_zone_creates_detection(self) -> None:
+        self.feed((1, [self.tracked("d1", "dog", 1, zones=["yard"])]))
+
+        self.assertEqual(self.review_summary(), [("new", "detection")])
+
+    def test_detection_label_outside_required_zone_creates_nothing(self) -> None:
+        self.feed((1, [self.tracked("d1", "dog", 1, zones=["driveway"])]))
+
+        self.assert_no_review()
+
+    def test_alert_label_ignores_detection_required_zones(self) -> None:
+        self.feed((1, [self.tracked("p1", "person", 1, zones=["driveway"])]))
+
+        self.assertEqual(self.review_summary(), [("new", "alert")])
+
+    def test_activity_outside_required_zone_after_alert_is_not_held(self) -> None:
+        self.feed(
+            (1, [self.tracked("p1", "person", 1, start_time=1)]),
+            (5, [self.tracked("d1", "dog", 5, start_time=5, zones=["driveway"])]),
+        )
+        self.assertEqual(
+            self.maintainer.active_review_segments[CAMERA].pending_detections, []
+        )
+
+        self.feed((2 + self.alert_cutoff, []))
+
+        self.assertEqual(
+            self.non_update_summary(), [("new", "alert"), ("end", "alert")]
+        )
+        self.assertIsNone(self.maintainer.active_review_segments.get(CAMERA))
+        self.assertEqual(len(self.thumbnails()), 1)
+
+    def test_activity_inside_required_zone_after_alert_is_split_out(self) -> None:
+        self.feed(
+            (1, [self.tracked("p1", "person", 1, start_time=1)]),
+            (5, [self.tracked("d1", "dog", 5, start_time=5, zones=["yard"])]),
+            (2 + self.alert_cutoff, []),
+        )
+
+        # the dog is past the detection cutoff, so it is ended right away
+        self.assertEqual(
+            self.non_update_summary(),
+            [
+                ("new", "alert"),
+                ("end", "alert"),
+                ("new", "detection"),
+                ("end", "detection"),
+            ],
+        )
+        detection = self.reviews()[-1]["after"]
+        self.assertEqual(detection["data"]["objects"], ["dog"])
+        self.assertEqual(detection["data"]["zones"], ["yard"])
+
+
+class TestDetectionLabels(ReviewFlowTestCase):
+    review_config = """
+    review:
+      detections:
+        labels:
+          - dog
+"""
+
+    def test_listed_label_creates_detection(self) -> None:
+        self.feed((1, [self.tracked("d1", "dog", 1)]))
+
+        self.assertEqual(self.review_summary(), [("new", "detection")])
+
+    def test_unlisted_label_creates_nothing(self) -> None:
+        self.feed((1, [self.tracked("c1", "cat", 1)]))
+
+        self.assert_no_review()
+
+    def test_unlisted_object_is_left_out_of_detection(self) -> None:
+        self.feed((1, [self.tracked("d1", "dog", 1), self.tracked("c1", "cat", 1)]))
+
+        self.assertEqual(self.review_summary(), [("new", "detection")])
+        self.assertEqual(self.reviews()[0]["after"]["data"]["objects"], ["dog"])
+
+
+class TestAlertsDisabled(ReviewFlowTestCase):
+    review_config = """
+    review:
+      alerts:
+        enabled: False
+"""
+
+    def test_alert_label_creates_detection(self) -> None:
+        self.feed((1, [self.tracked("p1", "person", 1)]))
+
+        self.assertEqual(self.review_summary(), [("new", "detection")])
+
+
+class TestDetectionsDisabled(ReviewFlowTestCase):
+    review_config = """
+    review:
+      detections:
+        enabled: False
+"""
+
+    def test_alert_label_creates_alert(self) -> None:
+        self.feed((1, [self.tracked("p1", "person", 1)]))
+
+        self.assertEqual(self.review_summary(), [("new", "alert")])
+
+    def test_non_alert_label_creates_nothing(self) -> None:
+        self.feed((1, [self.tracked("d1", "dog", 1)]))
+
+        self.assert_no_review()
+
+    def test_activity_after_alert_is_not_held(self) -> None:
+        self.feed(
+            (1, [self.tracked("p1", "person", 1, start_time=1)]),
+            (5, [self.tracked("d1", "dog", 5, start_time=5)]),
+        )
+        self.assertEqual(
+            self.maintainer.active_review_segments[CAMERA].pending_detections, []
+        )
+
+        self.feed((2 + self.alert_cutoff, []))
+
+        self.assertEqual(
+            self.non_update_summary(), [("new", "alert"), ("end", "alert")]
+        )
+        self.assertIsNone(self.maintainer.active_review_segments.get(CAMERA))
+        self.assertEqual(len(self.thumbnails()), 1)
+
+
+class TestAlertsAndDetectionsDisabled(ReviewFlowTestCase):
+    review_config = """
+    review:
+      alerts:
+        enabled: False
+      detections:
+        enabled: False
+"""
+
+    def test_nothing_is_created(self) -> None:
+        self.feed((1, [self.tracked("p1", "person", 1), self.tracked("d1", "dog", 1)]))
+
+        self.assert_no_review()
+
+
+class TestReviewLifecycle(ReviewFlowTestCase):
+    def test_detection_upgrades_to_alert_when_alert_object_appears(self) -> None:
+        self.feed(
+            (1, [self.tracked("d1", "dog", 1)]),
+            (2, [self.tracked("d1", "dog", 2), self.tracked("p1", "person", 2)]),
+        )
+
+        self.assertEqual(
+            self.review_summary(), [("new", "detection"), ("update", "alert")]
+        )
+        new, update = self.reviews()
+        self.assertEqual(new["after"]["id"], update["after"]["id"])
+        self.assertCountEqual(update["after"]["data"]["objects"], ["dog", "person"])
+
+    def test_alert_does_not_downgrade_when_only_detection_objects_remain(
+        self,
+    ) -> None:
+        self.feed(
+            (1, [self.tracked("p1", "person", 1), self.tracked("d1", "dog", 1)]),
+            (2, [self.tracked("d1", "dog", 2)]),
+            (3, [self.tracked("d1", "dog", 3)]),
+        )
+
+        self.assertEqual({severity for _, severity in self.review_summary()}, {"alert"})
+        self.assertEqual(
+            self.maintainer.active_review_segments[CAMERA].severity.value, "alert"
+        )
+
+    def test_alert_stays_open_until_cutoff(self) -> None:
+        self.feed(
+            (1, [self.tracked("p1", "person", 1)]),
+            (1 + self.alert_cutoff, []),
+        )
+
+        self.assertNotIn("end", [t for t, _ in self.review_summary()])
+        self.assertIsNotNone(self.maintainer.active_review_segments.get(CAMERA))
+
+    def test_alert_ends_after_cutoff_at_last_alert_activity(self) -> None:
+        self.feed(
+            (1, [self.tracked("p1", "person", 1)]),
+            (5, [self.tracked("p1", "person", 5)]),
+            (6, []),
+            (5 + self.alert_cutoff + 1, []),
+        )
+
+        end = self.reviews()[-1]
+        self.assertEqual(end["type"], "end")
+        self.assertEqual(end["after"]["severity"], "alert")
+        self.assertEqual(end["after"]["end_time"], 5)
+        self.assertIsNone(self.maintainer.active_review_segments.get(CAMERA))
+
+    def test_ongoing_alert_activity_extends_alert(self) -> None:
+        last_activity = 1 + self.alert_cutoff * 2
+        self.feed(
+            (1, [self.tracked("p1", "person", 1)]),
+            (
+                1 + self.alert_cutoff,
+                [self.tracked("p1", "person", 1 + self.alert_cutoff)],
+            ),
+            (last_activity, [self.tracked("p1", "person", last_activity)]),
+            (last_activity + 1, []),
+        )
+
+        self.assertNotIn("end", [t for t, _ in self.review_summary()])
+        self.assertEqual(
+            self.maintainer.active_review_segments[CAMERA].last_alert_time,
+            last_activity,
+        )
+
+    def test_detection_ends_after_cutoff_at_last_detection_activity(self) -> None:
+        self.feed(
+            (1, [self.tracked("d1", "dog", 1)]),
+            (5, [self.tracked("d1", "dog", 5)]),
+            (5 + self.detection_cutoff, []),
+        )
+        self.assertNotIn("end", [t for t, _ in self.review_summary()])
+
+        self.feed((5 + self.detection_cutoff + 1, []))
+
+        end = self.reviews()[-1]
+        self.assertEqual(end["type"], "end")
+        self.assertEqual(end["after"]["severity"], "detection")
+        self.assertEqual(end["after"]["end_time"], 5)
+        self.assertIsNone(self.maintainer.active_review_segments.get(CAMERA))
+
+    def test_stationary_object_does_not_extend_alert(self) -> None:
+        self.feed(
+            (1, [self.tracked("c1", "car", 1)]),
+            (2, [self.tracked("c1", "car", 2, stationary=True)]),
+            (
+                2 + self.alert_cutoff,
+                [self.tracked("c1", "car", 2 + self.alert_cutoff, stationary=True)],
+            ),
+        )
+
+        end = self.reviews()[-1]
+        self.assertEqual(end["type"], "end")
+        self.assertEqual(end["after"]["end_time"], 1)
+
+    def test_new_activity_after_end_creates_new_review(self) -> None:
+        self.feed(
+            (1, [self.tracked("p1", "person", 1)]),
+            (2 + self.alert_cutoff, []),
+            (3 + self.alert_cutoff, [self.tracked("d1", "dog", 3 + self.alert_cutoff)]),
+        )
+
+        self.assertEqual(
+            self.non_update_summary(),
+            [("new", "alert"), ("end", "alert"), ("new", "detection")],
+        )
+        ids = {r["after"]["id"] for r in self.reviews() if r["type"] != "update"}
+        self.assertEqual(len(ids), 2)
+
+    def test_alert_splits_into_detection_when_detection_activity_continues(
+        self,
+    ) -> None:
+        # the person leaves after the first frame while the dog keeps moving
+        dog_frames = [
+            (t, [self.tracked("d1", "dog", t, start_time=1)])
+            for t in range(11, 2 + self.alert_cutoff + 10, 10)
+        ]
+        self.feed(
+            (
+                1,
+                [
+                    self.tracked("p1", "person", 1, start_time=1),
+                    self.tracked("d1", "dog", 1, start_time=1),
+                ],
+            ),
+            *dog_frames,
+        )
+
+        self.assertEqual(
+            self.non_update_summary(),
+            [("new", "alert"), ("end", "alert"), ("new", "detection")],
+        )
+        alert_end = next(r for r in self.reviews() if r["type"] == "end")
+        self.assertEqual(alert_end["after"]["end_time"], 1)
+
+        detection = self.maintainer.active_review_segments[CAMERA]
+        self.assertEqual(detection.severity.value, "detection")
+        self.assertEqual(detection.start_time, 11)
+        self.assertEqual(list(detection.detections.values()), ["dog"])
+
+        # the detection ends once the dog stops moving
+        last_dog_time = dog_frames[-1][0]
+        self.feed((last_dog_time + self.detection_cutoff + 1, []))
+
+        end = self.reviews()[-1]
+        self.assertEqual(end["type"], "end")
+        self.assertEqual(end["after"]["severity"], "detection")
+        self.assertEqual(end["after"]["id"], detection.id)
+        self.assertEqual(end["after"]["end_time"], last_dog_time)
+
+    def test_detection_starting_after_alert_activity_is_split_out(self) -> None:
+        # the dog only shows up after the person has left
+        dog_frames = [
+            (t, [self.tracked("d1", "dog", t, start_time=11)])
+            for t in range(11, 2 + self.alert_cutoff + 10, 10)
+        ]
+        self.feed((1, [self.tracked("p1", "person", 1, start_time=1)]), *dog_frames)
+
+        self.assertEqual(
+            self.non_update_summary(),
+            [("new", "alert"), ("end", "alert"), ("new", "detection")],
+        )
+        alert_end = next(r for r in self.reviews() if r["type"] == "end")
+        self.assertEqual(alert_end["after"]["end_time"], 1)
+        self.assertEqual(alert_end["after"]["data"]["objects"], ["person"])
+
+        detection = self.maintainer.active_review_segments[CAMERA]
+        self.assertEqual(detection.severity.value, "detection")
+        self.assertEqual(detection.start_time, 11)
+        self.assertEqual(list(detection.detections.values()), ["dog"])
+
+    def test_detection_leaving_before_alert_cutoff_gets_detection(self) -> None:
+        # the dog comes and goes after the person left, all before the alert
+        # cutoff, so nothing is active when the alert ends
+        self.feed(
+            (1, [self.tracked("p1", "person", 1, start_time=1)]),
+            (11, [self.tracked("d1", "dog", 11, start_time=11)]),
+            (21, [self.tracked("d1", "dog", 21, start_time=11)]),
+            (31, []),
+            (2 + self.alert_cutoff, []),
+            (22 + self.detection_cutoff, []),
+        )
+
+        self.assertEqual(
+            self.non_update_summary(),
+            [
+                ("new", "alert"),
+                ("end", "alert"),
+                ("new", "detection"),
+                ("end", "detection"),
+            ],
+        )
+        ends = [r["after"] for r in self.reviews() if r["type"] == "end"]
+        self.assertEqual(ends[0]["end_time"], 1)
+        self.assertEqual(ends[0]["data"]["objects"], ["person"])
+        self.assertEqual(ends[1]["data"]["objects"], ["dog"])
+        self.assertEqual(ends[1]["start_time"], 11)
+        self.assertEqual(ends[1]["end_time"], 21)
+
+    def test_detection_older_than_detection_cutoff_gets_detection(self) -> None:
+        # the dog is gone longer than the detection cutoff by the time the
+        # alert ends, its activity still needs a detection
+        self.feed(
+            (1, [self.tracked("p1", "person", 1, start_time=1)]),
+            (3, [self.tracked("d1", "dog", 3, start_time=3)]),
+            (5, [self.tracked("d1", "dog", 5, start_time=3)]),
+            (6, []),
+            (2 + self.alert_cutoff, []),
+            (3 + self.alert_cutoff, []),
+        )
+        self.assertGreater(2 + self.alert_cutoff, 5 + self.detection_cutoff)
+
+        self.assertEqual(
+            self.non_update_summary(),
+            [
+                ("new", "alert"),
+                ("end", "alert"),
+                ("new", "detection"),
+                ("end", "detection"),
+            ],
+        )
+        ends = [r["after"] for r in self.reviews() if r["type"] == "end"]
+        self.assertEqual(ends[0]["end_time"], 1)
+        self.assertEqual(ends[0]["data"]["objects"], ["person"])
+        self.assertEqual(ends[1]["data"]["objects"], ["dog"])
+        self.assertEqual(ends[1]["start_time"], 3)
+        self.assertEqual(ends[1]["end_time"], 5)
+        self.assertIsNone(self.maintainer.active_review_segments.get(CAMERA))
+
+    def test_separate_detection_activity_after_alert_is_not_combined(self) -> None:
+        # the dog and cat are seen further apart than the detection cutoff
+        # while the alert is waiting to be cut off
+        cat_time = 3 + self.detection_cutoff + 6
+        self.assertLess(cat_time, 1 + self.alert_cutoff)
+        self.feed(
+            (1, [self.tracked("p1", "person", 1, start_time=1)]),
+            (3, [self.tracked("d1", "dog", 3, start_time=3)]),
+            (4, []),
+            (cat_time, [self.tracked("c1", "cat", cat_time, start_time=cat_time)]),
+            (2 + self.alert_cutoff, []),
+            (cat_time + self.detection_cutoff + 1, []),
+        )
+
+        self.assertEqual(
+            self.non_update_summary(),
+            [
+                ("new", "alert"),
+                ("end", "alert"),
+                ("new", "detection"),
+                ("end", "detection"),
+                ("new", "detection"),
+                ("end", "detection"),
+            ],
+        )
+        ends = [r["after"] for r in self.reviews() if r["type"] == "end"]
+        self.assertEqual(ends[0]["data"]["objects"], ["person"])
+        self.assertEqual(ends[1]["data"]["objects"], ["dog"])
+        self.assertEqual((ends[1]["start_time"], ends[1]["end_time"]), (3, 3))
+        self.assertEqual(ends[2]["data"]["objects"], ["cat"])
+        self.assertEqual(
+            (ends[2]["start_time"], ends[2]["end_time"]), (cat_time, cat_time)
+        )
+        for detection in ends[1:]:
+            self.assertTrue(Path(detection["thumb_path"]).is_file())
+            self.assertIsNotNone(detection["data"]["thumb_time"])
+
+    def test_resumed_alert_publishes_pending_detection_objects(self) -> None:
+        self.feed(
+            (1, [self.tracked("p1", "person", 1, start_time=1)]),
+            (5, [self.tracked("d1", "dog", 5, start_time=5)]),
+            (10, [self.tracked("p1", "person", 10, start_time=1)]),
+        )
+
+        latest = self.reviews()[-1]
+        self.assertEqual(latest["type"], "update")
+        self.assertEqual(latest["after"]["severity"], "alert")
+        self.assertCountEqual(latest["after"]["data"]["objects"], ["person", "dog"])
+
+    def test_split_detection_has_thumbnail_of_its_activity(self) -> None:
+        dog_frames = [
+            (t, [self.tracked("d1", "dog", t, start_time=11)])
+            for t in range(11, 2 + self.alert_cutoff + 10, 10)
+        ]
+        self.feed((1, [self.tracked("p1", "person", 1, start_time=1)]), *dog_frames)
+
+        new_detection = next(
+            r["after"]
+            for r in self.reviews()
+            if r["type"] == "new" and r["after"]["severity"] == "detection"
+        )
+        self.assertTrue(Path(new_detection["thumb_path"]).is_file())
+        # captured from the dog's first frame, not a later fallback frame
+        self.assertIsNotNone(new_detection["data"]["thumb_time"])
+        self.assertIn(
+            f"{CAMERA}_11",
+            [c.args[0] for c in self.maintainer.frame_manager.get.call_args_list],
+        )
+
+    def assert_force_end_publishes_pending_detection(self, topic: str) -> None:
+        # the dog is held as a pending detection when the alert is ended
+        self.feed(
+            (1, [self.tracked("p1", "person", 1, start_time=1)]),
+            (5, [self.tracked("d1", "dog", 5, start_time=5)]),
+        )
+
+        if topic == "remove":
+            # the config updater has already dropped the camera
+            self.maintainer.config.cameras.pop(CAMERA)
+
+        self.maintainer.config_subscriber.check_for_updates.side_effect = [
+            {topic: [CAMERA]}
+        ]
+        self.feed()
+
+        self.assertEqual(
+            self.non_update_summary(),
+            [
+                ("new", "alert"),
+                ("end", "alert"),
+                ("new", "detection"),
+                ("end", "detection"),
+            ],
+        )
+        end = self.reviews()[-1]["after"]
+        self.assertEqual(end["data"]["objects"], ["dog"])
+        self.assertEqual((end["start_time"], end["end_time"]), (5, 5))
+        self.assertIsNone(self.maintainer.active_review_segments.get(CAMERA))
+
+        # every thumbnail on disk belongs to a published review
+        published = {Path(r["after"]["thumb_path"]).name for r in self.reviews()}
+        self.assertEqual(self.thumbnails(), published)
+
+    def test_disabled_camera_publishes_pending_detections(self) -> None:
+        self.assert_force_end_publishes_pending_detection("enabled")
+
+    def test_removed_camera_publishes_pending_detections(self) -> None:
+        self.assert_force_end_publishes_pending_detection("remove")
+
+    def test_pending_thumbnail_removed_when_alert_resumes(self) -> None:
+        self.feed(
+            (1, [self.tracked("p1", "person", 1, start_time=1)]),
+            (5, [self.tracked("d1", "dog", 5, start_time=5)]),
+        )
+        alert = self.maintainer.active_review_segments[CAMERA]
+        pending_thumb = Path(alert.pending_detections[0].frame_path)
+        self.assertTrue(pending_thumb.is_file())
+
+        self.feed((10, [self.tracked("p1", "person", 10, start_time=1)]))
+
+        self.assertEqual(alert.pending_detections, [])
+        self.assertFalse(pending_thumb.exists())
+        self.assertEqual(self.thumbnails(), {Path(alert.frame_path).name})
+
+    def test_multiple_pending_objects_share_one_detection(self) -> None:
+        # objects within the detection cutoff of each other, whether seen
+        # together or later, make up one detection
+        self.feed(
+            (1, [self.tracked("p1", "person", 1, start_time=1)]),
+            (
+                5,
+                [
+                    self.tracked("d1", "dog", 5, start_time=5),
+                    self.tracked("c1", "cat", 5, start_time=5),
+                ],
+            ),
+            (20, [self.tracked("b1", "bird", 20, start_time=20)]),
+            (21, []),
+            (2 + self.alert_cutoff, []),
+            (21 + self.detection_cutoff, []),
+        )
+
+        self.assertEqual(
+            self.non_update_summary(),
+            [
+                ("new", "alert"),
+                ("end", "alert"),
+                ("new", "detection"),
+                ("end", "detection"),
+            ],
+        )
+        end = self.reviews()[-1]["after"]
+        self.assertCountEqual(end["data"]["objects"], ["dog", "cat", "bird"])
+        self.assertCountEqual(end["data"]["detections"], ["d1", "c1", "b1"])
+        self.assertEqual((end["start_time"], end["end_time"]), (5, 20))
+        # the thumbnail is framed on both objects seen together, the bird
+        # alone is fewer objects so it does not replace it
+        frames = [c.args[0] for c in self.maintainer.frame_manager.get.call_args_list]
+        self.assertIn(f"{CAMERA}_5", frames)
+        self.assertNotIn(f"{CAMERA}_20", frames)
+
+
+class TestSplitDetectionLabels(ReviewFlowTestCase):
+    review_config = """
+    review:
+      alerts:
+        labels:
+          - person
+"""
+
+    def test_split_detection_keeps_sub_labels_and_zones(self) -> None:
+        self.feed(
+            (1, [self.tracked("p1", "person", 1, start_time=1)]),
+            (
+                5,
+                [
+                    self.tracked(
+                        "d1",
+                        "dog",
+                        5,
+                        start_time=5,
+                        zones=["yard"],
+                        sub_label=("Rex", 0.95),
+                    ),
+                    self.tracked(
+                        "c1",
+                        "car",
+                        5,
+                        start_time=5,
+                        zones=["driveway"],
+                        sub_label=("fedex", 0.9),
+                    ),
+                ],
+            ),
+            (2 + self.alert_cutoff, []),
+        )
+
+        ends = [r["after"] for r in self.reviews() if r["type"] == "end"]
+        self.assertEqual(len(ends), 2)
+        alert_end, detection = ends
+        self.assertEqual(alert_end["data"]["objects"], ["person"])
+        self.assertEqual(alert_end["data"]["sub_labels"], [])
+        self.assertEqual(alert_end["data"]["zones"], [])
+
+        self.assertEqual(detection["severity"], "detection")
+        # attributes replace the label, other sub labels verify it
+        self.assertCountEqual(detection["data"]["objects"], ["dog-verified", "fedex"])
+        self.assertEqual(detection["data"]["verified_objects"], ["dog-verified"])
+        self.assertEqual(detection["data"]["sub_labels"], ["Rex"])
+        self.assertCountEqual(detection["data"]["zones"], ["yard", "driveway"])
+
+
+if __name__ == "__main__":
+    unittest.main()

frigate/test/test_sqlitevecq_embeddings.py

@@ -1,16 +1,24 @@
-"""Tests for embedding cleanup on the main Frigate database.
+"""Tests for embedding storage and cleanup on the main Frigate database.
 
 Embeddings are deleted whether or not semantic search is currently enabled, so
 the delete path has to tolerate databases where the vec0 tables were never
 created and installs where the sqlite-vec extension is unavailable.
+
+The write paths need the real extension, since the behavior under test belongs
+to vec0 itself, so those tests are skipped when it is not installed.
 """
 
 import os
+import struct
 import tempfile
 import unittest
 
+from peewee import OperationalError
+
 from frigate.db.sqlitevecq import SqliteVecQueueDatabase
 
+VEC_EXTENSION_PATH = "/usr/local/lib/vec0.so"
+
 
 class TestDeleteEmbeddings(unittest.TestCase):
     def setUp(self) -> None:
@@ -52,6 +60,21 @@ class TestDeleteEmbeddings(unittest.TestCase):
 
         self.assertEqual(self._thumbnail_ids(), ["b"])
 
+    def test_delete_failure_is_logged_not_raised(self) -> None:
+        self._create_thumbnails_table()
+        self.db.execute_sql(
+            """
+            CREATE TRIGGER vec_thumbnails_no_delete BEFORE DELETE ON vec_thumbnails
+            BEGIN SELECT RAISE(ABORT, 'delete blocked'); END
+            """
+        ).fetchall()
+
+        with self.assertLogs("frigate.db.sqlitevecq", level="ERROR") as logs:
+            self.db.delete_embeddings_thumbnail(event_ids=["a"])
+
+        self.assertIn("Failed to delete embeddings", logs.output[0])
+        self.assertEqual(self._thumbnail_ids(), ["a", "b"])
+
     def test_delete_skipped_without_extension(self) -> None:
         self._create_thumbnails_table()
         self.db.load_vec_extension = False
@@ -61,3 +84,104 @@ class TestDeleteEmbeddings(unittest.TestCase):
 
         # the vec0 tables cannot be written without the extension
         self.assertEqual(self._thumbnail_ids(), ["a", "b"])
+
+
+def _vector(value: float) -> bytes:
+    return struct.pack("768f", *([value] * 768))
+
+
+@unittest.skipUnless(
+    os.path.exists(VEC_EXTENSION_PATH), "sqlite-vec extension is not installed"
+)
+class TestEmbeddingsTableWrites(unittest.TestCase):
+    """Covers the vec0 writes behind semantic search reindexing."""
+
+    def setUp(self) -> None:
+        self.tmp_dir = tempfile.TemporaryDirectory()
+        self.db = SqliteVecQueueDatabase(
+            os.path.join(self.tmp_dir.name, "test.db"), load_vec_extension=True
+        )
+        self.db.start()
+        self.db.create_embeddings_tables()
+
+    def tearDown(self) -> None:
+        self.db.stop()
+        self.db.close()
+        self.tmp_dir.cleanup()
+
+    def _vec_tables(self) -> list[str]:
+        return [
+            row[0]
+            for row in self.db.execute_sql(
+                "SELECT name FROM sqlite_master WHERE name LIKE 'vec_%' ORDER BY name"
+            )
+        ]
+
+    def _make_legacy(self, table: str) -> None:
+        # sqlite-vec added the _info shadow table in 0.1.6, so tables written by
+        # Frigate 0.17 and earlier do not have one
+        self.db.execute_sql(f"DROP TABLE {table}_info").fetchall()
+
+    def _stored(self, table: str, column: str, event_id: str) -> str | None:
+        row = self.db.execute_sql(
+            f"SELECT vec_to_json({column}) FROM {table} WHERE id = ?", (event_id,)
+        ).fetchone()
+        return row[0] if row else None
+
+    def test_write_error_is_raised(self) -> None:
+        # queued writes hide their exception in the returned cursor
+        with self.assertRaises(OperationalError):
+            self.db.execute_write("INSERT INTO vec_missing(id) VALUES ('a')")
+
+    def test_drop_tables_removes_legacy_tables(self) -> None:
+        self._make_legacy("vec_thumbnails")
+        self._make_legacy("vec_descriptions")
+
+        self.db.drop_embeddings_tables()
+
+        self.assertEqual(self._vec_tables(), [])
+
+    def test_drop_tables_without_any_tables_does_not_raise(self) -> None:
+        self.db.drop_embeddings_tables()
+
+        self.db.drop_embeddings_tables()
+
+    def test_upsert_replaces_existing_embedding(self) -> None:
+        self.db.upsert_embeddings(
+            "vec_thumbnails", "thumbnail_embedding", {"evt1": _vector(0.01)}
+        )
+
+        self.db.upsert_embeddings(
+            "vec_thumbnails", "thumbnail_embedding", {"evt1": _vector(0.99)}
+        )
+
+        stored = self._stored("vec_thumbnails", "thumbnail_embedding", "evt1")
+        self.assertTrue(stored.startswith("[0.990000"), stored)
+
+    def test_upsert_keeps_one_row_per_event(self) -> None:
+        for _ in range(3):
+            self.db.upsert_embeddings(
+                "vec_descriptions", "description_embedding", {"evt1": _vector(0.5)}
+            )
+
+        count = self.db.execute_sql(
+            "SELECT count(*) FROM vec_descriptions WHERE id = 'evt1'"
+        ).fetchone()[0]
+        self.assertEqual(count, 1)
+
+    def test_reindex_cycle_rewrites_legacy_tables(self) -> None:
+        """The 0.18 upgrade path: old vectors in, new vectors out."""
+        self.db.upsert_embeddings(
+            "vec_thumbnails", "thumbnail_embedding", {"evt1": _vector(0.01)}
+        )
+        self._make_legacy("vec_thumbnails")
+        self._make_legacy("vec_descriptions")
+
+        self.db.drop_embeddings_tables()
+        self.db.create_embeddings_tables()
+        self.db.upsert_embeddings(
+            "vec_thumbnails", "thumbnail_embedding", {"evt1": _vector(0.99)}
+        )
+
+        stored = self._stored("vec_thumbnails", "thumbnail_embedding", "evt1")
+        self.assertTrue(stored.startswith("[0.990000"), stored)

frigate/test/test_timeline.py

@@ -0,0 +1,66 @@
+import unittest
+from unittest.mock import MagicMock, patch
+
+from frigate.events.types import EventStateEnum
+from frigate.models import Timeline
+from frigate.timeline import TimelineProcessor
+
+
+def make_event(has_clip: bool, has_snapshot: bool) -> dict:
+    return {
+        "id": "event-1",
+        "frame_time": 1000.0,
+        "box": [0, 0, 10, 10],
+        "region": [0, 0, 100, 100],
+        "label": "car",
+        "sub_label": None,
+        "score": 0.8,
+        "has_clip": has_clip,
+        "has_snapshot": has_snapshot,
+        "current_zones": [],
+        "stationary": False,
+        "attributes": {},
+        "current_attributes": [],
+    }
+
+
+class TestTimelineProcessor(unittest.TestCase):
+    def setUp(self):
+        camera_config = MagicMock()
+        camera_config.detect.width = 1280
+        camera_config.detect.height = 720
+        config = MagicMock()
+        config.cameras.get.return_value = camera_config
+        self.processor = TimelineProcessor(config, MagicMock(), MagicMock())
+
+    @patch.object(Timeline, "insert")
+    def test_unsaved_event_writes_no_timeline_rows(self, insert):
+        event = make_event(has_clip=False, has_snapshot=False)
+        self.processor.handle_object_detection(
+            "front", EventStateEnum.start, None, event
+        )
+        self.processor.handle_object_detection(
+            "front", EventStateEnum.end, event, event
+        )
+
+        insert.assert_not_called()
+        self.assertEqual(self.processor.pre_event_cache, {})
+
+    @patch.object(Timeline, "insert")
+    def test_cached_entries_flush_when_event_is_saved(self, insert):
+        start = make_event(has_clip=False, has_snapshot=False)
+        self.processor.handle_object_detection(
+            "front", EventStateEnum.start, None, start
+        )
+        insert.assert_not_called()
+
+        end = make_event(has_clip=True, has_snapshot=False)
+        self.processor.handle_object_detection("front", EventStateEnum.end, start, end)
+
+        class_types = [c.args[0][Timeline.class_type] for c in insert.call_args_list]
+        self.assertEqual(class_types, ["visible", "gone"])
+        self.assertEqual(self.processor.pre_event_cache, {})
+
+
+if __name__ == "__main__":
+    unittest.main()