docs

environment_vars is exported into os.environ, and is_go2rtc_arbitrary_exec_allowed read os.environ live, so the config file could enable exec sources. Snapshot the variable at import, which runs before any config is loaded.

Makefile

@@ -1,7 +1,7 @@
 default_target: local
 
 COMMIT_HASH := $(shell git log -1 --pretty=format:"%h"|tail -1)
-VERSION = 0.18.1
+VERSION = 0.19.0
 IMAGE_REPO ?= ghcr.io/blakeblackshear/frigate
 GITHUB_REF_NAME ?= $(shell git rev-parse --abbrev-ref HEAD)
 BOARDS= #Initialized empty

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

@@ -3,13 +3,12 @@
 import json
 import os
 import sys
-from pathlib import Path
 from typing import Any
 
 from ruamel.yaml import YAML
 
 sys.path.insert(0, "/opt/frigate")
-from frigate.config.env import substitute_frigate_vars
+from frigate.config.env import apply_config_env_vars, substitute_frigate_vars
 from frigate.const import (
     BIRDSEYE_PIPE,
     LIBAVFORMAT_VERSION_MAJOR,
@@ -25,15 +24,6 @@ sys.path.remove("/opt/frigate")
 
 yaml = YAML()
 
-FRIGATE_ENV_VARS = {k: v for k, v in os.environ.items() if k.startswith("FRIGATE_")}
-# read docker secret files as env vars too
-if os.path.isdir("/run/secrets"):
-    for secret_file in os.listdir("/run/secrets"):
-        if secret_file.startswith("FRIGATE_"):
-            FRIGATE_ENV_VARS[secret_file] = (
-                Path(os.path.join("/run/secrets", secret_file)).read_text().strip()
-            )
-
 config_file = find_config_file()
 
 try:
@@ -47,6 +37,20 @@ try:
 except FileNotFoundError:
     config: dict[str, Any] = {}
 
+# No validator runs here, so install environment_vars ourselves. FRIGATE_
+# names only: anything else lands in os.environ, where the exec gate reads
+# GO2RTC_ALLOW_ARBITRARY_EXEC.
+config_env_vars = config.get("environment_vars")
+apply_config_env_vars(
+    {
+        key: value
+        for key, value in config_env_vars.items()
+        if str(key).startswith("FRIGATE_")
+    }
+    if isinstance(config_env_vars, dict)
+    else {}
+)
+
 go2rtc_config: dict[str, Any] = config.get("go2rtc", {})
 
 # Need to enable CORS for go2rtc so the frigate integration / card work automatically
@@ -113,7 +117,7 @@ for name in list(go2rtc_config.get("streams", {})):
 
     if isinstance(stream, str):
         try:
-            formatted_stream = stream.format(**FRIGATE_ENV_VARS)
+            formatted_stream = substitute_frigate_vars(stream)
             if is_restricted_go2rtc_source(formatted_stream):
                 print(
                     f"[ERROR] Stream '{name}' uses a restricted source (echo/expr/exec) which is disabled by default for security. "
@@ -122,7 +126,7 @@ for name in list(go2rtc_config.get("streams", {})):
                 del go2rtc_config["streams"][name]
                 continue
             go2rtc_config["streams"][name] = formatted_stream
-        except KeyError as e:
+        except ValueError as e:
             print(
                 "[ERROR] Invalid substitution found, see https://docs.frigate.video/configuration/restream#advanced-restream-configurations for more info."
             )
@@ -132,7 +136,7 @@ for name in list(go2rtc_config.get("streams", {})):
         filtered_streams = []
         for i, stream_item in enumerate(stream):
             try:
-                formatted_stream = stream_item.format(**FRIGATE_ENV_VARS)
+                formatted_stream = substitute_frigate_vars(stream_item)
                 if is_restricted_go2rtc_source(formatted_stream):
                     print(
                         f"[ERROR] Stream '{name}' item {i + 1} uses a restricted source (echo/expr/exec) which is disabled by default for security. "
@@ -141,7 +145,7 @@ for name in list(go2rtc_config.get("streams", {})):
                     continue
 
                 filtered_streams.append(formatted_stream)
-            except KeyError as e:
+            except ValueError as e:
                 print(
                     "[ERROR] Invalid substitution found, see https://docs.frigate.video/configuration/restream#advanced-restream-configurations for more info."
                 )

docker/main/rootfs/usr/local/nginx/conf/nginx.conf

@@ -75,6 +75,12 @@ http {
         vod_align_segments_to_key_frames on;
         vod_manifest_segment_durations_mode accurate;
         vod_ignore_edit_list on;
+        # short leading segments at each playlist start; sources start at
+        # the seek target, so the ladder applies to every seek. Only
+        # effective when clips declare real keyFrameDurations
+        vod_bootstrap_segment_durations 1000;
+        vod_bootstrap_segment_durations 2000;
+        vod_bootstrap_segment_durations 4000;
         vod_segment_duration 10000;
 
         # MPEG-TS settings (not used when fMP4 is enabled, kept for reference)
@@ -150,9 +156,7 @@ http {
             include auth_request.conf;
             types {
                 video/mp4 mp4;
-                image/jpeg jpg jpeg;
-                image/png png;
-                image/webp webp;
+                image/jpeg jpg;
             }
 
             expires 7d;

docs/docs/configuration/advanced/reference.md

@@ -56,17 +56,6 @@ mqtt:
   # 2 = exactly once
   qos: 0
 
-# Optional: Detectors configuration. Defaults to a single CPU detector
-detectors:
-  # Required: name of the detector
-  detector_name:
-    # Required: type of the detector
-    # Frigate provides many types, see https://docs.frigate.video/configuration/object_detectors for more details (default: shown below)
-    # Additional detector types can also be plugged in.
-    # Detectors may require additional configuration.
-    # Refer to the Detectors configuration page for more information.
-    type: cpu
-
 # Optional: Database configuration
 database:
   # The path to store the SQLite DB (default: shown below)
@@ -157,44 +146,56 @@ auth:
       - front_door
       - back_yard
 
-# Optional: model modifications
+# Optional: object detection models. Defaults to a single model on a CPU detector.
 # NOTE: The default values are for the EdgeTPU detector.
 # Other detectors will require the model config to be set.
-model:
-  # Required: path to the model. Frigate+ models use plus://<model_id> (default: automatic based on detector)
-  path: /edgetpu_model.tflite
-  # Required: path to the labelmap (default: shown below)
-  labelmap_path: /labelmap.txt
-  # Required: Object detection model input width (default: shown below)
-  width: 320
-  # Required: Object detection model input height (default: shown below)
-  height: 320
-  # Required: Object detection model input colorspace
-  # Valid values are rgb, bgr, or yuv. (default: shown below)
-  input_pixel_format: rgb
-  # Required: Object detection model input tensor format
-  # Valid values are nhwc, nchw, hwnc, or hwcn (default: shown below)
-  input_tensor: nhwc
-  # Optional: Data type of the model input tensor
-  # Valid values are float, float_denorm, or int (default: shown below)
-  input_dtype: int
-  # Required: Object detection model architecture, used by detectors that support more
-  # than one model type (openvino, onnx, rknn, memryx, axengine, synaptics, and others)
-  # Valid values are ssd, yolox, yolonas, yolo-generic, rfdetr, dfine (default: shown below)
-  model_type: ssd
-  # Required: Label name modifications. These are merged into the standard labelmap.
-  labelmap:
-    2: vehicle
-  # Optional: Map of object labels to their attribute labels (default: depends on model)
-  attributes_map:
-    person:
-      - amazon
-      - face
-    car:
-      - amazon
-      - fedex
-      - license_plate
-      - ups
+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.
+    # Valid values are all, indoor, outdoor, indoor_thermal, outdoor_thermal
+  - scene: all
+    # 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
+    # devices must use the same detector. Listing the same device more than once
+    # runs additional inference processes on it.
+    devices:
+      - edgetpu:pci:0
+    # Required: path to the model. Frigate+ models use plus://<model_id> (default: automatic based on detector)
+    path: /edgetpu_model.tflite
+    # Required: path to the labelmap (default: shown below)
+    labelmap_path: /labelmap.txt
+    # Required: Object detection model input width (default: shown below)
+    width: 320
+    # Required: Object detection model input height (default: shown below)
+    height: 320
+    # Required: Object detection model input colorspace
+    # Valid values are rgb, bgr, or yuv. (default: shown below)
+    input_pixel_format: rgb
+    # Required: Object detection model input tensor format
+    # Valid values are nhwc, nchw, hwnc, or hwcn (default: shown below)
+    input_tensor: nhwc
+    # Optional: Data type of the model input tensor
+    # Valid values are float, float_denorm, or int (default: shown below)
+    input_dtype: int
+    # Required: Object detection model architecture, used by detectors that support more
+    # than one model type (openvino, onnx, rknn, memryx, axengine, synaptics, and others)
+    # Valid values are ssd, yolox, yolonas, yolo-generic, rfdetr, dfine (default: shown below)
+    model_type: ssd
+    # Required: Label name modifications. These are merged into the standard labelmap.
+    labelmap:
+      2: vehicle
+    # Optional: Map of object labels to their attribute labels (default: depends on model)
+    attributes_map:
+      person:
+        - amazon
+        - face
+      car:
+        - amazon
+        - fedex
+        - license_plate
+        - ups
 
 # Optional: Audio Events Configuration
 # NOTE: Can be overridden at the camera level
@@ -217,6 +218,8 @@ audio:
     - fire_alarm
     - speech
     - yell
+  # Optional: Audio label name modifications. These are merged into the standard audio labelmap.
+  labelmap: {}
   # Optional: Filters to configure detection.
   filters:
     # Label that matches label in listen config.
@@ -251,11 +254,15 @@ birdseye:
   # Optional: Encoding quality of the mpeg1 feed (default: shown below)
   # 1 is the highest quality, and 31 is the lowest. Lower quality feeds utilize less CPU resources.
   quality: 8
-  # Optional: Mode of the view. Available options are: objects, motion, and continuous
-  #   objects - cameras are included if they have had a tracked object within the last 30 seconds
-  #   motion - cameras are included if motion was detected in the last 30 seconds
-  #   continuous - all cameras are included always
-  mode: objects
+  # Optional: Activity types that include cameras in Birdseye (default: shown below)
+  # Multiple activity types can be listed at the same time.
+  #   continuous:  all cameras are included always
+  #   motion:      included if motion was detected within the inactivity threshold
+  #   all_objects: included if a tracked object was present within the inactivity threshold
+  #   alerts:      included while an alert review item is in progress
+  #   detections:  included while a detection review item is in progress
+  modes:
+    - all_objects
   # Optional: Threshold for camera activity to stop showing camera (default: shown below)
   inactivity_threshold: 30
   # Optional: Configure the birdseye layout
@@ -286,7 +293,9 @@ 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-audio-aac
+    record: preset-record-generic
+    # Optional: output args for sub stream record streams (default: the record output args above)
+    # record_sub: preset-record-generic
   # 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
@@ -306,6 +315,10 @@ detect:
   width: 1280
   # 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)
+  # Valid values are all, indoor, outdoor, indoor_thermal, outdoor_thermal
+  scene: outdoor
   # 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
@@ -637,6 +650,42 @@ record:
       #       For example, if the camera retain mode is "motion", the segments without motion are
       #       never stored, so setting the mode to "all" here won't bring them back.
       mode: motion
+  # Optional: Sub stream recording settings
+  # Records a second, lower quality stream for quality selection during playback
+  # and extended low quality retention. Requires the record_sub role to be assigned
+  # to one of the camera's inputs.
+  sub:
+    # Optional: Enable sub stream recording (default: shown below)
+    # NOTE: Recording must also be enabled for sub stream recording to run.
+    enabled: False
+    # Optional: Continuous retention settings for sub stream recordings
+    continuous:
+      # Optional: Number of days to retain sub stream recordings regardless of tracked objects or motion (default: shown below)
+      days: 0
+    # Optional: Motion retention settings for sub stream recordings
+    motion:
+      # Optional: Number of days to retain sub stream recordings triggered by motion (default: shown below)
+      days: 0
+    # Optional: Retention settings for sub stream recordings of alerts
+    # NOTE: Pre and post capture windows are taken from the main alerts config above.
+    alerts:
+      # Required: Retention days (default: shown below)
+      days: 10
+      # Optional: Mode for retention. (default: shown below)
+      #   all - save all sub stream recording segments for alerts regardless of activity
+      #   motion - save all sub stream recording segments for alerts with any detected motion
+      #   active_objects - save all sub stream recording segments for alerts with active/moving objects
+      mode: motion
+    # Optional: Retention settings for sub stream recordings of detections
+    # NOTE: Pre and post capture windows are taken from the main detections config above.
+    detections:
+      # Required: Retention days (default: shown below)
+      days: 10
+      # Optional: Mode for retention. (default: shown below)
+      #   all - save all sub stream recording segments for detections regardless of activity
+      #   motion - save all sub stream recording segments for detections with any detected motion
+      #   active_objects - save all sub stream recording segments for detections with active/moving objects
+      mode: motion
 
 # Optional: Configuration for the snapshots written to the clips directory for each tracked object
 # Timestamp, bounding_box, crop and height settings are applied by default to API requests for snapshots.
@@ -888,7 +937,7 @@ cameras:
         # Required: the path to the stream
         # NOTE: path may include environment variables or docker secrets, which must begin with 'FRIGATE_' and be referenced in {}
         - path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
-          # Required: list of roles for this stream. valid values are: audio,detect,record
+          # Required: list of roles for this stream. valid values are: audio,detect,record,record_sub
           # NOTICE: In addition to assigning the audio, detect, and record roles
           # they must also be enabled in the camera config.
           roles:

docs/docs/configuration/advanced/system.md

@@ -63,15 +63,9 @@ go2rtc:
 
 ### `environment_vars`
 
-This section can be used to set environment variables for those unable to modify the environment of the container, like within Home Assistant OS. Docker users should set environment variables in their `docker run` command (`-e FRIGATE_MQTT_PASSWORD=secret`) or `docker-compose.yml` file (`environment:` section) instead. Note that values set here are stored in plain text in your config file, so if the goal is to keep credentials out of your configuration, use Docker environment variables or Docker secrets instead.
+This section sets environment variables in the Frigate process for those unable to modify the environment of the container, like within Home Assistant OS. It's meant for process settings such as `LIBVA_DRIVER_NAME` or the TensorFlow thread counts below. Docker users should set environment variables in their `docker run` command (`-e LIBVA_DRIVER_NAME=i965`) or `docker-compose.yml` file (`environment:` section) instead. Values set here are stored in plain text in your config file, so credentials belong in `secrets.yaml`, Docker environment variables, or Docker secrets instead.
 
-Variables prefixed with `FRIGATE_` can be referenced in config fields that support environment variable substitution (such as MQTT host and credentials, camera stream URLs, and ONVIF host and credentials) using the `{FRIGATE_VARIABLE_NAME}` syntax.
-
-:::note
-
-The `go2rtc` section is an exception. go2rtc runs as a separate process, so its stream definitions can only be substituted with variables that exist in the container's environment (set via Docker `-e`, the `environment:` section of `docker-compose.yml`, or Docker secrets). Variables defined in the `environment_vars` block above are not available to go2rtc streams. Home Assistant app users, who cannot set container environment variables, must instead put credentials directly in their go2rtc stream URLs.
-
-:::
+Names prefixed with `FRIGATE_` set here also take part in `{FRIGATE_VARIABLE_NAME}` substitution (see [below](#substitution-sources-and-precedence)), but `secrets.yaml` is the better home for them.
 
 <ConfigTabs>
 <TabItem value="ui">
@@ -80,23 +74,17 @@ Navigate to <NavPath path="Settings > System > Environment variables" /> to add
 
 | Field             | Description                                               |
 | ----------------- | --------------------------------------------------------- |
-| **Variable name** | The environment variable name (e.g., `FRIGATE_MQTT_USER`) |
+| **Variable name** | The environment variable name (e.g., `LIBVA_DRIVER_NAME`) |
 | **Value**         | The value for the variable                                |
 
-Variables defined here can be referenced elsewhere in your configuration using the `{FRIGATE_VARIABLE_NAME}` syntax.
+Names prefixed with `FRIGATE_` can also be referenced elsewhere in your configuration using the `{FRIGATE_VARIABLE_NAME}` syntax.
 
 </TabItem>
 <TabItem value="yaml">
 
 ```yaml
 environment_vars:
-  FRIGATE_MQTT_USER: my_mqtt_user
-  FRIGATE_MQTT_PASSWORD: my_mqtt_password
-
-mqtt:
-  host: "{FRIGATE_MQTT_HOST}"
-  user: "{FRIGATE_MQTT_USER}"
-  password: "{FRIGATE_MQTT_PASSWORD}"
+  LIBVA_DRIVER_NAME: i965
 ```
 
 </TabItem>
@@ -130,6 +118,29 @@ environment_vars:
 </TabItem>
 </ConfigTabs>
 
+### `secrets.yaml`
+
+A `secrets.yaml` file in your config directory is an additional source of `FRIGATE_` variables, for installs that can't set container environment variables or mount Docker secrets. It's a flat map of names to values, and it is never read or written by the Frigate UI:
+
+```yaml
+FRIGATE_CAM_USER: viewer
+FRIGATE_CAM_PASS: "p@ss w0rd"
+FRIGATE_MQTT_HOST: mqtt.internal.example
+```
+
+Names must start with `FRIGATE_`, and nesting is not supported. `secrets.yaml` feeds `{FRIGATE_VARIABLE_NAME}` substitution, so the handful of variables Frigate reads straight from the process environment, such as `FRIGATE_JWT_SECRET`, still need a container environment variable or a Docker secret.
+
+### Substitution sources and precedence
+
+The same `{FRIGATE_VARIABLE_NAME}` placeholder resolves from four sources, listed strongest first. When a name is defined in more than one, the highest wins and a warning is logged:
+
+1. Docker secrets or the directory named by `CREDENTIALS_DIRECTORY` (defaults to `/run/secrets`)
+2. Container environment variables
+3. `secrets.yaml`
+4. The `environment_vars` block above
+
+Referencing a name that no source defines is a config validation error naming the field.
+
 ### `database`
 
 Tracked object and recording information is managed in a sqlite database at `/config/frigate.db`. If that database is deleted, recordings will be orphaned and will need to be cleaned up manually. They also won't show up in the Media Browser within Home Assistant.
@@ -177,7 +188,7 @@ Custom models may also require different input tensor formats. The colorspace co
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detectors and model" /> and open the **Custom Model** tab to configure the model path, dimensions, and input format.
+Navigate to <NavPath path="Settings > System > Detection models" /> and, on the model you want to change, open the **Custom Model** tab to configure the model path, dimensions, and input format.
 
 | Field                                         | Description                          |
 | --------------------------------------------- | ------------------------------------ |
@@ -192,12 +203,14 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and open
 
 ```yaml
 # Optional: model config
-model:
-  path: /path/to/model
-  width: 320
-  height: 320
-  input_tensor: "nhwc"
-  input_pixel_format: "bgr"
+models:
+  - devices:
+      - openvino:GPU
+    path: /path/to/model
+    width: 320
+    height: 320
+    input_tensor: "nhwc"
+    input_pixel_format: "bgr"
 ```
 
 </TabItem>
@@ -214,15 +227,15 @@ If the labelmap is customized then the labels used for alerts will need to be ad
 The labelmap can be customized to your needs. A common reason to do this is to combine multiple object types that are easily confused when you don't need to be as granular such as car/truck. By default, truck is renamed to car because they are often confused. You cannot add new object types, but you can change the names of existing objects in the model.
 
 ```yaml
-model:
-  labelmap:
-    2: vehicle
-    3: vehicle
-    5: vehicle
-    7: vehicle
-    15: animal
-    16: animal
-    17: animal
+models:
+  - labelmap:
+      2: vehicle
+      3: vehicle
+      5: vehicle
+      7: vehicle
+      15: animal
+      16: animal
+      17: animal
 ```
 
 Note that if you rename objects in the labelmap, you will also need to update your `objects -> track` list as well.

docs/docs/configuration/audio_detectors.md

@@ -114,6 +114,30 @@ audio:
 </TabItem>
 </ConfigTabs>
 
+#### Grouping Audio Labels
+
+Related audio classes can be grouped under one label by mapping their numeric
+class IDs to the same name. Add the grouped name to `listen` and use it for any
+corresponding filter:
+
+```yaml
+audio:
+  listen:
+    - dogs
+  labelmap:
+    69: dogs # dog
+    70: dogs # bark
+    75: dogs # whimper_dog
+  filters:
+    dogs:
+      threshold: 0.8
+```
+
+Class IDs are zero-based indices in
+[`audio-labelmap.txt`](https://github.com/blakeblackshear/frigate/blob/dev/audio-labelmap.txt),
+so each ID is one less than the displayed file line number.
+Audio label mappings are separate from the object detector's `model.labelmap`.
+
 ### Common Audio Labels
 
 The labelmap includes hundreds of sound types. The labels below are the ones most users may find practical, grouped by what they're typically used for. Use the exact label string from the left column in your `listen` config, or search for the label in the Frigate UI directly.

docs/docs/configuration/birdseye.md

@@ -18,13 +18,17 @@ Each camera tile in Birdseye is composed from the frames of the stream assigned
 
 ## Birdseye Behavior
 
-### Birdseye Modes
+### Birdseye Activity Types
 
-Birdseye offers different modes to customize which cameras show under which circumstances.
+Birdseye offers independent activity types that control when cameras are shown. Multiple activity types can be listed together.
 
-- **continuous:** All cameras are always included
-- **motion:** Cameras that have detected motion within the last 30 seconds are included
-- **objects:** Cameras that have tracked an active object within the last 30 seconds are included
+- **continuous:** The camera is always included
+- **motion:** The camera is included when motion was detected within the last 30 seconds
+- **all_objects:** The camera is included when a tracked object is present, active or stationary
+- **alerts:** The camera is included while an alert review item is in progress
+- **detections:** The camera is included while a detection review item is in progress
+
+`alerts` and `detections` follow the review item's own lifetime, so the camera is removed as soon as the review item ends. Which objects qualify for each is set in [review configuration](./review.md).
 
 ### Custom Birdseye Icon
 
@@ -39,27 +43,29 @@ To include a camera in Birdseye view only for specific circumstances, or exclude
 
 **Global settings:** Navigate to <NavPath path="Settings > System > Birdseye" /> to configure the default Birdseye behavior for all cameras.
 
-**Per-camera overrides:** Navigate to <NavPath path="Settings > Camera configuration > Birdseye" /> to override the mode or disable Birdseye for a specific camera.
+**Per-camera overrides:** Navigate to <NavPath path="Settings > Camera configuration > Birdseye" /> to override the activity types or disable Birdseye for a specific camera.
 
-| Field               | Description                                                   |
-| ------------------- | ------------------------------------------------------------- |
-| **Enable Birdseye** | Whether this camera appears in Birdseye view                  |
-| **Tracking mode**   | When to show the camera: `continuous`, `motion`, or `objects` |
+| Field                  | Description                                                |
+| ---------------------- | ---------------------------------------------------------- |
+| **Enable Birdseye**    | Whether this camera appears in Birdseye view               |
+| **Activity types**     | Conditions that determine when to show the camera          |
 
 </TabItem>
 <TabItem value="yaml">
 
-```yaml {8-10,12-14}
+```yaml {10-12,15-16}
 # Include all cameras by default in Birdseye view
 birdseye:
   enabled: True
-  mode: continuous
+  modes:
+    - continuous
 
 cameras:
   front:
-    # Only include the "front" camera in Birdseye view when objects are detected
+    # Only include the "front" camera in Birdseye view when an alert is in progress
     birdseye:
-      mode: objects
+      modes:
+        - alerts
   back:
     # Exclude the "back" camera from Birdseye view
     birdseye:
@@ -71,7 +77,7 @@ cameras:
 
 ### Birdseye Inactivity
 
-By default birdseye shows all cameras that have had the configured activity in the last 30 seconds. This threshold can be configured.
+By default birdseye shows all cameras that have had the configured activity in the last 30 seconds. This threshold can be configured, and applies to the `motion` and `all_objects` activity types only.
 
 <ConfigTabs>
 <TabItem value="ui">
@@ -140,7 +146,8 @@ Navigate to <NavPath path="Settings > System > Birdseye" /> and in the **Camera
 # Include all cameras by default in Birdseye view
 birdseye:
   enabled: True
-  mode: continuous
+  modes:
+    - continuous
 
 cameras:
   front:

docs/docs/configuration/config.md

@@ -100,7 +100,7 @@ VS Code supports JSON schemas for automatically validating configuration files.
 
 ## Environment Variable Substitution
 
-Frigate supports the use of environment variables starting with `FRIGATE_` **only** where specifically indicated in the [reference config](./advanced/reference.md). For example, the following values can be replaced at runtime by using environment variables:
+Frigate supports the use of environment variables starting with `FRIGATE_` **only** where specifically indicated in the [reference config](./advanced/reference.md). See [substitution sources and precedence](./advanced/system.md#substitution-sources-and-precedence) for where those values can come from, including `secrets.yaml`. For example, the following values can be replaced at runtime by using environment variables:
 
 ```yaml
 mqtt:
@@ -154,7 +154,7 @@ Here are some common starter configuration examples. These can be configured thr
 
 1. Navigate to <NavPath path="Settings > System > MQTT" /> and configure the MQTT connection to your Home Assistant Mosquitto broker
 2. Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Raspberry Pi (H.264)`
-3. Navigate to <NavPath path="Settings > System > Detectors and model" /> and add a detector with **Type** `EdgeTPU` and **Device** `usb`
+3. Navigate to <NavPath path="Settings > System > Detection models" /> and select **Coral EdgeTPU (USB)** from the **Hardware** dropdown
 4. Navigate to <NavPath path="Settings > Global configuration > Recording" /> and set **Enable recording** to on, **Motion retention > Retention days** to `7`, **Alert retention > Event retention > Retention days** to `30`, **Alert retention > Event retention > Retention mode** to `motion`, **Detection retention > Event retention > Retention days** to `30`, **Detection retention > Event retention > Retention mode** to `motion`
 5. Navigate to <NavPath path="Settings > Global configuration > Snapshots" /> and set **Enable snapshots** to on, **Snapshot retention > Default retention** to `30`
 6. Navigate to <NavPath path="Settings > Global configuration > Camera management" /> and add your camera with the appropriate RTSP stream URL
@@ -172,10 +172,9 @@ mqtt:
 ffmpeg:
   hwaccel_args: preset-rpi-64-h264
 
-detectors:
-  coral:
-    type: edgetpu
-    device: usb
+models:
+  - devices:
+      - edgetpu:usb
 
 record:
   enabled: True
@@ -233,7 +232,7 @@ cameras:
 
 1. Navigate to <NavPath path="Settings > System > MQTT" /> and set **Enable MQTT** to off
 2. Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `VAAPI (Intel/AMD GPU)`
-3. Navigate to <NavPath path="Settings > System > Detectors and model" /> and add a detector with **Type** `EdgeTPU` and **Device** `usb`
+3. Navigate to <NavPath path="Settings > System > Detection models" /> and select **Coral EdgeTPU (USB)** from the **Hardware** dropdown
 4. Navigate to <NavPath path="Settings > Global configuration > Recording" /> and set **Enable recording** to on, **Motion retention > Retention days** to `7`, **Alert retention > Event retention > Retention days** to `30`, **Alert retention > Event retention > Retention mode** to `motion`, **Detection retention > Event retention > Retention days** to `30`, **Detection retention > Event retention > Retention mode** to `motion`
 5. Navigate to <NavPath path="Settings > Global configuration > Snapshots" /> and set **Enable snapshots** to on, **Snapshot retention > Default retention** to `30`
 6. Navigate to <NavPath path="Settings > Global configuration > Camera management" /> and add your camera with the appropriate RTSP stream URL
@@ -249,10 +248,9 @@ mqtt:
 ffmpeg:
   hwaccel_args: preset-vaapi
 
-detectors:
-  coral:
-    type: edgetpu
-    device: usb
+models:
+  - devices:
+      - edgetpu:usb
 
 record:
   enabled: True
@@ -310,8 +308,8 @@ cameras:
 
 1. Navigate to <NavPath path="Settings > System > MQTT" /> and configure the connection to your MQTT broker
 2. Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `VAAPI (Intel/AMD GPU)`
-3. Navigate to <NavPath path="Settings > System > Detectors and model" /> and add a detector with **Type** `openvino` and **Device** `AUTO`
-4. On the same page, in the **Custom Model** tab, configure the OpenVINO model path and settings
+3. Navigate to <NavPath path="Settings > System > Detection models" /> and select **Intel GPU** from the **Hardware** dropdown
+4. On the same model, open the **Custom Model** tab and configure the OpenVINO model path and settings
 5. Navigate to <NavPath path="Settings > Global configuration > Recording" /> and set **Enable recording** to on, **Motion retention > Retention days** to `7`, **Alert retention > Event retention > Retention days** to `30`, **Alert retention > Event retention > Retention mode** to `motion`, **Detection retention > Event retention > Retention days** to `30`, **Detection retention > Event retention > Retention mode** to `motion`
 6. Navigate to <NavPath path="Settings > Global configuration > Snapshots" /> and set **Enable snapshots** to on, **Snapshot retention > Default retention** to `30`
 7. Navigate to <NavPath path="Settings > Global configuration > Camera management" /> and add your camera with the appropriate RTSP stream URL
@@ -329,15 +327,12 @@ mqtt:
 ffmpeg:
   hwaccel_args: preset-vaapi
 
-detectors:
-  ov:
-    type: openvino
-    device: AUTO
-
-model:
-  width: 300
-  height: 300
-  input_tensor: nhwc
+models:
+  - devices:
+      - openvino:AUTO
+    width: 300
+    height: 300
+    input_tensor: nhwc
   input_pixel_format: bgr
   path: /openvino-model/ssdlite_mobilenet_v2.xml
   labelmap_path: /openvino-model/coco_91cl_bkgr.txt

docs/docs/configuration/ffmpeg_presets.md

@@ -106,3 +106,5 @@ Output arguments are passed to FFmpeg after your camera source and control how r
 | preset-record-mjpeg              | Record - MJPEG Cameras          | Record an MJPEG stream              | Restreaming the MJPEG stream is recommended instead                                                                                                               |
 | preset-record-jpeg               | Record - JPEG Cameras           | Record a live JPEG                  | Restreaming the live JPEG is recommended instead                                                                                                                  |
 | preset-record-ubiquiti           | Record - Ubiquiti Cameras       | Record a Ubiquiti stream with audio | Handles Ubiquiti's non-standard audio format                                                                                                                      |
+
+These presets apply to the `record` output args. If [sub stream recording](/configuration/record#sub-stream-recording) is enabled, the same args are used for the `record_sub` role unless `output_args.record_sub` is set, which accepts the same presets and manual args.

docs/docs/configuration/hardware_acceleration_video.md

@@ -498,7 +498,7 @@ cameras:
 
 ## Synaptics
 
-Hardware accelerated video de-/encoding is supported on Synaptics SL-series SoC.
+Hardware accelerated video de-/encoding is supported on Synpatics SL-series SoC.
 
 ### Prerequisites
 

docs/docs/configuration/license_plate_recognition.md

@@ -8,7 +8,7 @@ import TabItem from "@theme/TabItem";
 import NavPath from "@site/src/components/NavPath";
 import FaqItem from "@site/src/components/FaqItem";
 
-Frigate can recognize license plates on vehicles and automatically add the detected characters to the `recognized_license_plate` field or a [known](#matching) name as a `sub_label` to tracked objects of type `car`, `motorcycle`, `bus`, `truck`, `school_bus`, or `garbage_truck`, depending on which of those labels your model detects. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street.
+Frigate can recognize license plates on vehicles and automatically add the detected characters to the `recognized_license_plate` field or a [known](#matching) name as a `sub_label` to tracked objects of type `car` or `motorcycle`. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street.
 
 LPR works best when the license plate is clearly visible to the camera. For moving vehicles, Frigate continuously refines the recognition process, keeping the most confident result. When a vehicle becomes stationary, LPR continues to run for a short time after to attempt recognition.
 
@@ -24,7 +24,7 @@ When a plate is recognized, the details are:
 - Viewable in the Details pane in Review/History.
 - Viewable in the Tracked Object Details pane in Explore (sub labels and recognized license plates).
 - Filterable through the More Filters menu in Explore.
-- Published via the `frigate/events` MQTT topic as a `sub_label` ([known](#matching)) or `recognized_license_plate` (unknown) for the vehicle tracked object.
+- Published via the `frigate/events` MQTT topic as a `sub_label` ([known](#matching)) or `recognized_license_plate` (unknown) for the `car` or `motorcycle` tracked object.
 - Published via the `frigate/tracked_object_update` MQTT topic with `name` (if [known](#matching)) and `plate`.
 
 ## Model Requirements
@@ -35,7 +35,7 @@ Users without a model that detects license plates can still run LPR. Frigate use
 
 :::note
 
-In the default mode, Frigate's LPR needs to first detect a vehicle before it can recognize a license plate. If you're using a dedicated LPR camera and have a zoomed-in view where a vehicle will not be detected, you can still run LPR, but the configuration parameters will differ from the default mode. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section below.
+In the default mode, Frigate's LPR needs to first detect a `car` or `motorcycle` before it can recognize a license plate. If you're using a dedicated LPR camera and have a zoomed-in view where a `car` or `motorcycle` will not be detected, you can still run LPR, but the configuration parameters will differ from the default mode. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section below.
 
 :::
 
@@ -86,7 +86,7 @@ cameras:
 </TabItem>
 </ConfigTabs>
 
-For non-dedicated LPR cameras, ensure that your camera is configured to detect vehicle objects, and that a vehicle is actually being detected by Frigate. Otherwise, LPR will not run. The object types that can carry a plate are defined by your model's `attributes_map`, so if your model detects other vehicle labels, you can add them there.
+For non-dedicated LPR cameras, ensure that your camera is configured to detect objects of type `car` or `motorcycle`, and that a car or motorcycle is actually being detected by Frigate. Otherwise, LPR will not run.
 
 Like the other real-time processors in Frigate, license plate recognition runs on the camera stream defined by the `detect` role in your config. To ensure optimal performance, select a suitable resolution for this stream in your camera's firmware that fits your specific scene and requirements.
 
@@ -158,7 +158,7 @@ lpr:
 
 Navigate to <NavPath path="Settings > Enrichments > License plate recognition" />.
 
-- **Known plates**: Assign custom `sub_label` values to vehicle objects when a recognized plate matches a known value. These labels appear in the UI, filters, and notifications. Unknown plates are still saved but are added to the `recognized_license_plate` field rather than the `sub_label`.
+- **Known plates**: Assign custom `sub_label` values to `car` and `motorcycle` objects when a recognized plate matches a known value. These labels appear in the UI, filters, and notifications. Unknown plates are still saved but are added to the `recognized_license_plate` field rather than the `sub_label`.
 - **Match distance**: Allows for minor variations (missing/incorrect characters) when matching a detected plate to a known plate. For example, setting to `1` allows a plate `ABCDE` to match `ABCBE` or `ABCD`. This parameter will _not_ operate on known plates that are defined as regular expressions.
 
 </TabItem>
@@ -316,7 +316,7 @@ lpr:
 
 :::note
 
-If a camera is configured to detect vehicles but you don't want Frigate to run LPR for that camera, disable LPR at the camera level:
+If a camera is configured to detect `car` or `motorcycle` but you don't want Frigate to run LPR for that camera, disable LPR at the camera level:
 
 <ConfigTabs>
 <TabItem value="ui">
@@ -378,10 +378,10 @@ 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 > Confidence threshold** | Set to `0.7`        |
+| Field                                          | Description         |
+| ---------------------------------------------- | ------------------- |
+| **Objects to track**                           | Add `license_plate` |
+| **Object filters > License Plate > Threshold** | Set to `0.7`        |
 
 Navigate to <NavPath path="Settings > Camera configuration > Motion detection" />.
 
@@ -456,7 +456,7 @@ With this setup:
 - Snapshots will have license plate bounding boxes on them.
 - The `frigate/events` MQTT topic will publish tracked object updates.
 - Debug view will display `license_plate` bounding boxes.
-- If you are using a Frigate+ model and want to submit images from your dedicated LPR camera for model training and fine-tuning, annotate both the vehicle and the `license_plate` in the snapshots on the Frigate+ website, even if the vehicle is barely visible.
+- If you are using a Frigate+ model and want to submit images from your dedicated LPR camera for model training and fine-tuning, annotate both the `car` / `motorcycle` and the `license_plate` in the snapshots on the Frigate+ website, even if the car is barely visible.
 
 ### Using the Secondary LPR Pipeline (Without Frigate+)
 
@@ -611,9 +611,9 @@ If you are still having issues detecting plates, start with a basic configuratio
 
 </FaqItem>
 
-<FaqItem id="can-i-run-lpr-without-detecting-car-or-motorcycle-objects" question={<>Can I run LPR without detecting vehicle objects?</>}>
+<FaqItem id="can-i-run-lpr-without-detecting-car-or-motorcycle-objects" question={<>Can I run LPR without detecting <code>car</code> or <code>motorcycle</code> objects?</>}>
 
-In normal LPR mode, Frigate requires a vehicle to be detected first before recognizing a license plate. If you have a dedicated LPR camera, you can change the camera `type` to `"lpr"` to use the Dedicated LPR Camera algorithm. This comes with important caveats, though. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section above.
+In normal LPR mode, Frigate requires a `car` or `motorcycle` to be detected first before recognizing a license plate. If you have a dedicated LPR camera, you can change the camera `type` to `"lpr"` to use the Dedicated LPR Camera algorithm. This comes with important caveats, though. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section above.
 
 </FaqItem>
 
@@ -699,7 +699,7 @@ lpr:
 4. Ensure the characters on detected plates are being _recognized_.
    - Check the **Plate recognition** inference time in Enrichment metrics (<NavPath path="System metrics > Enrichments" />). High inference times (> 100ms) could lead to poor recognition results, especially for dedicated LPR cameras where the plate crosses the frame quickly.
    - Enable `debug_save_plates` to save images of detected text on plates to the clips directory (`/media/frigate/clips/lpr`). Ensure these images are readable and the text is clear.
-   - Watch the debug view to see plates recognized in real-time. For non-dedicated LPR cameras, the vehicle's label will change to the recognized plate when LPR is enabled and working.
+   - Watch the debug view to see plates recognized in real-time. For non-dedicated LPR cameras, the `car` or `motorcycle` label will change to the recognized plate when LPR is enabled and working.
    - Adjust `recognition_threshold` settings per the suggestions [above](#advanced-configuration).
 
 </FaqItem>
@@ -714,13 +714,13 @@ LPR's performance impact depends on your hardware. Ensure you have at least 4GB
 
 The YOLOv9 license plate detector model will run (and the metric will appear) if you've enabled LPR but haven't defined `license_plate` as an object to track, either at the global or camera level.
 
-If you are detecting vehicles on cameras where you don't want to run LPR, make sure you disable LPR it at the camera level. And if you do want to run LPR on those cameras, make sure you define `license_plate` as an object to track.
+If you are detecting `car` or `motorcycle` on cameras where you don't want to run LPR, make sure you disable LPR it at the camera level. And if you do want to run LPR on those cameras, make sure you define `license_plate` as an object to track.
 
 </FaqItem>
 
 <FaqItem id="it-looks-like-frigate-picked-up-my-cameras-timestamp-or-overlay-text-as-the-license-plate-how-can-i-prevent-this" question="It looks like Frigate picked up my camera's timestamp or overlay text as the license plate. How can I prevent this?">
 
-This could happen if vehicles travel close to your camera's timestamp or overlay text. You could either move the text through your camera's firmware, or apply a mask to it in Frigate.
+This could happen if cars or motorcycles travel close to your camera's timestamp or overlay text. You could either move the text through your camera's firmware, or apply a mask to it in Frigate.
 
 If you are using a model that natively detects `license_plate`, add an _object mask_ of type `license_plate` and a _motion mask_ over your text.
 

docs/docs/configuration/object_detectors.md

@@ -68,12 +68,66 @@ Frigate supports multiple different detectors that work on different types of ha
 
 :::note
 
-Multiple detectors can not be mixed for object detection (ex: OpenVINO and Coral EdgeTPU can not be used for object detection at the same time).
+A single model can not be spread across different detector types (ex: OpenVINO and Coral EdgeTPU can not run the same model at the same time). Configuring more than one model, each on its own detector type, is supported.
 
 This does not affect using hardware for accelerating other tasks such as [semantic search](./semantic_search.md)
 
 :::
 
+### Configuring models and hardware
+
+Object detection is configured with a `models` list. Each entry describes one model and the hardware it runs on:
+
+```yaml
+models:
+  - devices:
+      - openvino:GPU
+    path: /config/model_cache/yolov9-s.onnx
+    model_type: yolo-generic
+    width: 320
+    height: 320
+```
+
+Each entry in `devices` is a detector type, optionally followed by a colon and a device for that detector, such as `edgetpu:pci:0`, `openvino:NPU`, or `tensorrt:0`. The per-detector sections below document the device values each one accepts. Listing several devices runs the model on all of them, and listing the **same** device more than once runs additional inference processes against it, which can improve throughput on hardware that keeps up with more than one stream:
+
+```yaml
+models:
+  - devices:
+      - openvino:GPU
+      - openvino:GPU
+```
+
+Coral EdgeTPU and MemryX accelerators can only be opened by one process, so those devices can not be repeated.
+
+### 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`:
+
+```yaml
+models:
+  - scene: outdoor
+    path: plus://your-outdoor-model
+    devices:
+      - edgetpu:pci:0
+  - scene: indoor
+    path: /config/model_cache/indoor.onnx
+    model_type: yolo-generic
+    devices:
+      - openvino:GPU
+
+cameras:
+  driveway:
+    detect:
+      scene: outdoor
+    ...
+  hallway:
+    detect:
+      scene: indoor
+    ...
+```
+
+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.
+
 ### Choosing a model size
 
 Along with picking a detector for your hardware, you will choose a model's **input resolution** (such as `320x320` or `640x640`) and, for model families like YOLOv9, a **variant size** (`tiny`, `small`, etc.). Both affect the balance between accuracy and the inference time your hardware can sustain.
@@ -92,11 +146,11 @@ The best detection accuracy comes from a model trained on images that look like
 
 # Officially Supported Detectors
 
-Frigate provides a number of builtin detector types. By default, Frigate will use a single CPU detector. Other detectors may require additional configuration as described below. When using multiple detectors they will run in dedicated processes, but pull from a common queue of detection requests from across all cameras.
+Frigate provides a number of builtin detector types. By default, Frigate will use a single CPU detector. Other detectors may require additional configuration as described below. Each of a model's devices runs in a dedicated process, and they pull from a common queue of detection requests from the cameras assigned to that model.
 
 ## Edge TPU Detector
 
-The Edge TPU detector type runs TensorFlow Lite models utilizing the Google Coral delegate for hardware acceleration. To configure an Edge TPU detector, set the `"type"` attribute to `"edgetpu"`.
+The Edge TPU detector type runs TensorFlow Lite models utilizing the Google Coral delegate for hardware acceleration. To use it, prefix a model's device with `edgetpu`.
 
 The Edge TPU device can be specified using the `"device"` attribute according to the [Documentation for the TensorFlow Lite Python API](https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api). If not set, the delegate will use the first device it finds.
 
@@ -111,16 +165,15 @@ See [common Edge TPU troubleshooting steps](/troubleshooting/edgetpu) if the Edg
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **EdgeTPU** from the detector type dropdown and click **Add**, then set device to `usb`.
+Navigate to <NavPath path="Settings > System > Detection models" /> and select **Coral EdgeTPU (USB)** from the **Hardware** dropdown.
 
 </TabItem>
 <TabItem value="yaml">
 
 ```yaml
-detectors:
-  coral:
-    type: edgetpu
-    device: usb
+models:
+  - devices:
+      - edgetpu:usb
 ```
 
 </TabItem>
@@ -131,19 +184,16 @@ detectors:
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **EdgeTPU** from the detector type dropdown and click **Add** to add multiple detectors, specifying `usb:0` and `usb:1` as the device for each.
+Navigate to <NavPath path="Settings > System > Detection models" /> and select **Coral EdgeTPU (USB)** from the **Hardware** dropdown and check each Coral the model should run on.
 
 </TabItem>
 <TabItem value="yaml">
 
 ```yaml
-detectors:
-  coral1:
-    type: edgetpu
-    device: usb:0
-  coral2:
-    type: edgetpu
-    device: usb:1
+models:
+  - devices:
+      - edgetpu:usb:0
+      - edgetpu:usb:1
 ```
 
 </TabItem>
@@ -156,16 +206,15 @@ _warning: may have [compatibility issues](https://github.com/blakeblackshear/fri
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **EdgeTPU** from the detector type dropdown and click **Add**, then leave the device field empty.
+Navigate to <NavPath path="Settings > System > Detection models" /> and select the **Coral EdgeTPU** entry from the **Hardware** dropdown.
 
 </TabItem>
 <TabItem value="yaml">
 
 ```yaml
-detectors:
-  coral:
-    type: edgetpu
-    device: ""
+models:
+  - devices:
+      - 'edgetpu:'
 ```
 
 </TabItem>
@@ -176,16 +225,15 @@ detectors:
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **EdgeTPU** from the detector type dropdown and click **Add**, then set device to `pci`.
+Navigate to <NavPath path="Settings > System > Detection models" /> and select **Coral EdgeTPU (PCIe)** from the **Hardware** dropdown.
 
 </TabItem>
 <TabItem value="yaml">
 
 ```yaml
-detectors:
-  coral:
-    type: edgetpu
-    device: pci
+models:
+  - devices:
+      - edgetpu:pci
 ```
 
 </TabItem>
@@ -196,19 +244,16 @@ detectors:
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **EdgeTPU** from the detector type dropdown and click **Add** to add multiple detectors, specifying `pci:0` and `pci:1` as the device for each.
+Navigate to <NavPath path="Settings > System > Detection models" /> and select **Coral EdgeTPU (PCIe)** from the **Hardware** dropdown and check each Coral the model should run on.
 
 </TabItem>
 <TabItem value="yaml">
 
 ```yaml
-detectors:
-  coral1:
-    type: edgetpu
-    device: pci:0
-  coral2:
-    type: edgetpu
-    device: pci:1
+models:
+  - devices:
+      - edgetpu:pci:0
+      - edgetpu:pci:1
 ```
 
 </TabItem>
@@ -219,19 +264,16 @@ detectors:
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **EdgeTPU** from the detector type dropdown and click **Add** to add multiple detectors with different device types (e.g., `usb` and `pci`).
+Navigate to <NavPath path="Settings > System > Detection models" /> and select **Coral EdgeTPU (USB)** from the **Hardware** dropdown. USB and PCIe Corals are listed as separate hardware, so mixing the two on one model has to be done in YAML.
 
 </TabItem>
 <TabItem value="yaml">
 
 ```yaml
-detectors:
-  coral_usb:
-    type: edgetpu
-    device: usb
-  coral_pci:
-    type: edgetpu
-    device: pci
+models:
+  - devices:
+      - edgetpu:usb
+      - edgetpu:pci
 ```
 
 </TabItem>
@@ -273,7 +315,7 @@ Hailo8 supports all models in the Hailo Model Zoo that include HailoRT post-proc
 
 ## OpenVINO Detector
 
-The OpenVINO detector type runs an OpenVINO IR model on AMD and Intel CPUs, Intel GPUs and Intel NPUs. To configure an OpenVINO detector, set the `"type"` attribute to `"openvino"`.
+The OpenVINO detector type runs an OpenVINO IR model on AMD and Intel CPUs, Intel GPUs and Intel NPUs. To use it, prefix a model's device with `openvino`.
 
 The OpenVINO device to be used is specified using the `"device"` attribute according to the naming conventions in the [Device Documentation](https://docs.openvino.ai/2025/openvino-workflow/running-inference/inference-devices-and-modes.html). The most common devices are `CPU`, `GPU`, or `NPU`.
 
@@ -286,13 +328,10 @@ OpenVINO is supported on 6th Gen Intel platforms (Skylake) and newer. It will al
 When using many cameras one detector may not be enough to keep up. Multiple detectors can be defined assuming GPU resources are available. An example configuration would be:
 
 ```yaml
-detectors:
-  ov_0:
-    type: openvino
-    device: GPU # or NPU
-  ov_1:
-    type: openvino
-    device: GPU # or NPU
+models:
+  - devices:
+      - openvino:GPU # or NPU
+      - openvino:GPU # or NPU
 ```
 
 :::
@@ -313,6 +352,12 @@ Intel NPUs cannot be used under Home Assistant OS, which does not include the NP
 
 ## Apple Silicon detector
 
+:::warning
+
+The network-based detectors (Deepstack and the Apple Silicon client) are being reworked. Their extra options no longer have a place in the config, so only the endpoint carried in the device string is honored right now: Deepstack ignores `api_key` and `api_timeout`, and the Apple Silicon client ignores `request_timeout_ms` and `linger_ms`. Anything else is dropped when your config is migrated.
+
+:::
+
 The NPU in Apple Silicon can't be accessed from within a container, so the [Apple Silicon detector client](https://github.com/frigate-nvr/apple-silicon-detector) must first be setup. It is recommended to use the Frigate docker image with `-standard-arm64` suffix, for example `ghcr.io/blakeblackshear/frigate:stable-standard-arm64`.
 
 ### Setup {#setup-apple-silicon}
@@ -453,11 +498,10 @@ If the correct build is used for your GPU then the GPU will be detected and used
 When using many cameras one detector may not be enough to keep up. Multiple detectors can be defined assuming GPU resources are available. An example configuration would be:
 
 ```yaml
-detectors:
-  onnx_0:
-    type: onnx
-  onnx_1:
-    type: onnx
+models:
+  - devices:
+      - onnx
+      - onnx
 ```
 
 :::
@@ -470,7 +514,7 @@ detectors:
 
 ## CPU Detector (not recommended)
 
-The CPU detector type runs a TensorFlow Lite model utilizing the CPU without hardware acceleration. It is recommended to use a hardware accelerated detector type instead for better performance. To configure a CPU based detector, set the `"type"` attribute to `"cpu"`.
+The CPU detector type runs a TensorFlow Lite model utilizing the CPU without hardware acceleration. It is recommended to use a hardware accelerated detector type instead for better performance. To use it, set a model's device to `cpu`.
 
 :::danger
 
@@ -480,7 +524,7 @@ The CPU detector is not recommended for general use. If you do not have GPU or E
 
 The number of threads used by the interpreter can be specified using the `"num_threads"` attribute, and defaults to `3.`
 
-A TensorFlow Lite model is provided in the container at `/cpu_model.tflite` and is used by this detector type by default. To provide your own model, bind mount the file into the container and provide the path with `model.path`.
+A TensorFlow Lite model is provided in the container at `/cpu_model.tflite` and is used by this detector type by default. To provide your own model, bind mount the file into the container and provide the path with the model's `path`.
 
 ### Configuration {#configuration-cpu}
 
@@ -490,6 +534,12 @@ When using CPU detectors, you can add one CPU detector per camera. Adding more d
 
 ## Deepstack / CodeProject.AI Server Detector
 
+:::warning
+
+The network-based detectors (Deepstack and the Apple Silicon client) are being reworked. Their extra options no longer have a place in the config, so only the endpoint carried in the device string is honored right now: Deepstack ignores `api_key` and `api_timeout`, and the Apple Silicon client ignores `request_timeout_ms` and `linger_ms`. Anything else is dropped when your config is migrated.
+
+:::
+
 The Deepstack / CodeProject.AI Server detector for Frigate allows you to integrate Deepstack and CodeProject.AI object detection capabilities into Frigate. CodeProject.AI and DeepStack are open-source AI platforms that can be run on various devices such as the Raspberry Pi, Nvidia Jetson, and other compatible hardware. It is important to note that the integration is performed over the network, so the inference times may not be as fast as native Frigate detectors, but it still provides an efficient and reliable solution for object detection and tracking.
 
 ### Setup {#setup-deepstack}
@@ -552,7 +602,7 @@ For detailed instructions on compiling models, refer to the [MemryX Compiler](ht
 
 3. Depending on the model, the compiler may also generate a cropped post-processing network. If present, it will be named with the suffix `_post.onnx`.
 
-4. Bind-mount the `.zip` file into the container and specify its path using `model.path` in your config.
+4. Bind-mount the `.zip` file into the container and specify its path using the model's `path` in your config.
 
 5. Update `labelmap_path` to match your custom model's labels.
 
@@ -682,13 +732,10 @@ If no custom model is provided, the RKNN detector downloads a default model from
 When using many cameras one detector may not be enough to keep up. Multiple detectors can be defined assuming NPU resources are available. An example configuration would be:
 
 ```yaml
-detectors:
-  rknn_0:
-    type: rknn
-    num_cores: 0
-  rknn_1:
-    type: rknn
-    num_cores: 0
+models:
+  - devices:
+      - rknn:0
+      - rknn:0
 ```
 
 :::

docs/docs/configuration/object_filters.md

@@ -45,10 +45,10 @@ 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 > Minimum confidence**   | Minimum score for a single detection to initiate tracking        |
-| **Object filters > Person > Confidence threshold** | Minimum computed (median) score to be considered a true positive |
+| Field                                   | Description                                                      |
+| --------------------------------------- | ---------------------------------------------------------------- |
+| **Object filters > Person > Min Score** | Minimum score for a single detection to initiate tracking        |
+| **Object filters > Person > 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.
 
@@ -103,12 +103,12 @@ 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 > Minimum object area**  | Minimum 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 > Minimum aspect ratio** | Minimum width/height ratio of the bounding box                           |
-| **Object filters > Person > Maximum aspect ratio** | Maximum width/height ratio of the bounding box                           |
+| Field                                   | Description                                                              |
+| --------------------------------------- | ------------------------------------------------------------------------ |
+| **Object filters > Person > Min 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 > Min Ratio** | Minimum width/height ratio of the bounding box                           |
+| **Object filters > Person > Max 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

@@ -70,14 +70,14 @@ 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 > Minimum object area**  | Minimum 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 > Minimum aspect ratio** | Minimum width/height ratio of the bounding box                           |
-| **Object filters > Person > Maximum aspect ratio** | Maximum width/height ratio of the bounding box                           |
-| **Object filters > Person > Minimum confidence**   | Minimum score for the object to initiate tracking                        |
-| **Object filters > Person > Confidence threshold** | Minimum computed score to be considered a true positive                  |
+| Field                                   | Description                                                              |
+| --------------------------------------- | ------------------------------------------------------------------------ |
+| **Object filters > Person > Min 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 > Min 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 > Min Score** | Minimum score for the object to initiate tracking                        |
+| **Object filters > Person > 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

@@ -191,12 +191,14 @@ cameras:
     detect:
       enabled: false
     record:
-      enabled: true
+      enabled: false
     profiles:
       away:
         enabled: true
         detect:
           enabled: true
+        record:
+          enabled: true
       home:
         enabled: false
 ```
@@ -249,12 +251,6 @@ 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/record.md

@@ -9,7 +9,7 @@ import NavPath from "@site/src/components/NavPath";
 
 Recordings can be enabled and are stored at `/media/frigate/recordings`. The folder structure for the recordings is `YYYY-MM-DD/HH/<camera_name>/MM.SS.mp4` in **UTC time**. These recordings are written directly from your camera stream without re-encoding. Each camera supports a configurable retention policy. Frigate chooses the largest matching retention value between the recording retention and the tracked object retention when determining if a recording should be removed.
 
-New recording segments are written from the camera stream to cache, they are only moved to disk if they pass a validation check and match the setup recording retention policy.
+New recording segments are written from the camera stream to cache, they are only moved to disk if they match the setup recording retention policy.
 
 :::tip
 
@@ -275,6 +275,163 @@ record:
 
 This configuration will retain recording segments that overlap with alerts and detections for 10 days. Because multiple tracked objects can reference the same recording segments, this avoids storing duplicate footage for overlapping tracked objects and reduces overall storage needs.
 
+## Sub Stream Recording
+
+In addition to the main recording stream, Frigate can record a second, lower quality stream for each camera. This serves two purposes:
+
+- **Quality selection during playback**: A quality selector (`Auto`, `Original`, or `Low`) appears in History view for cameras with sub stream recording enabled. `Original` and `Low` play only that stream's recordings. Time ranges where the selected stream has no footage are skipped during playback, and the selector notes when the selected stream has no recordings at all in the viewed time range. With `Auto` (the default), playback prefers the original quality and automatically falls back to the low quality stream when the connection cannot keep up, or for time ranges where the original recordings have expired. The selector shows each stream's video codec and audio details beneath the options; footage recorded by older Frigate versions shows no details.
+- **Extended retention**: Sub stream recordings have their own retention settings, fully independent of the main recordings. By giving the low quality recordings a longer retention period, you can keep weeks or months of low quality history using a fraction of the storage, and that history remains playable after the main recordings expire. Playback falls back to the low quality recordings automatically, and the timeline shows a muted treatment for time ranges where only low quality footage remains.
+
+### Configuring sub stream recording
+
+Sub stream recording uses the `record_sub` input role. This role can be assigned to the same input as `detect`, so in the common case where detect already uses the camera's sub stream, no additional camera connection is needed. Like the main recording stream, sub stream segments are copied directly from the camera stream without re-encoding, so the recording quality is determined by the source stream.
+
+The following examples keep 7 days of full quality continuous recordings and 60 days of low quality continuous recordings:
+
+<ConfigTabs>
+<TabItem value="ui">
+
+Navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" /> and select the camera.
+
+- In **Camera inputs**, enable the **Record (Sub Stream)** role on the stream you want to record at low quality, commonly the same stream that has the **Detect** role. Only one stream may have this role, and it cannot be assigned to the same stream as the **Record** role.
+
+Navigate to <NavPath path="Settings > Camera configuration > Recording" /> and select the camera.
+
+- Set **Enable recording** to on
+- Set **Continuous retention > Retention days** to `7`
+- Set **Sub stream recording > Enable sub stream recording** to on
+- Set **Sub stream recording > Sub stream continuous retention > Retention days** to `60`
+
+The camera setup wizard also offers the **Record (Sub Stream)** role when assigning stream roles for a newly added camera.
+
+</TabItem>
+<TabItem value="yaml">
+
+```yaml
+cameras:
+  front_door:
+    ffmpeg:
+      inputs:
+        - path: rtsp://camera/main
+          roles:
+            - record
+        - path: rtsp://camera/sub
+          roles:
+            - detect
+            - record_sub
+    record:
+      enabled: true
+      continuous:
+        days: 7
+      sub:
+        enabled: true
+        continuous:
+          days: 60
+```
+
+If your camera does not provide a suitable sub stream (or the sub stream is already used at a resolution you don't want to record), you can use a go2rtc transcode as the source for `record_sub` instead:
+
+```yaml
+go2rtc:
+  streams:
+    front_door: rtsp://camera/main
+    front_door_lq: ffmpeg:front_door#video=h264#width=854#hardware
+
+cameras:
+  front_door:
+    ffmpeg:
+      inputs:
+        - path: rtsp://127.0.0.1:8554/front_door
+          input_args: preset-rtsp-restream
+          roles:
+            - detect
+            - record
+        - path: rtsp://127.0.0.1:8554/front_door_lq
+          input_args: preset-rtsp-restream
+          roles:
+            - record_sub
+    record:
+      enabled: true
+      continuous:
+        days: 7
+      sub:
+        enabled: true
+        continuous:
+          days: 60
+```
+
+</TabItem>
+</ConfigTabs>
+
+The `record.sub` config supports the same retention structure as the main recording config: `continuous`, `motion`, `alerts`, and `detections` each with their own `days` (and `mode` for alerts and detections). The pre-capture and post-capture windows for alerts and detections are taken from the main `record.alerts` and `record.detections` config. Extending `sub.alerts.days` or `sub.detections.days` beyond the main values also keeps those review items visible in the review timeline for the longer window, with playback falling back to the low quality stream once the main recordings expire.
+
+:::note
+
+Recording must be enabled (`record.enabled`) for sub stream recording to run, and Frigate will fail to start if `record.sub.enabled` is set without a `record_sub` role assigned to one of the camera's inputs.
+
+:::
+
+### How Auto picks a quality
+
+`Auto` measures throughput on every segment download and compares it against the original stream's bitrate (computed from the recorded footage itself). Playback drops to the low quality stream when any of these happen:
+
+- A freeze lasts 4 seconds (10 seconds when it starts within 2 seconds of a seek, since the seek target is rarely buffered), or freezes total 7 seconds within the last minute.
+- 3 downloads in a row measure below the original bitrate plus 10%, dropping quality before a stall ever becomes visible.
+- No first frame appears within 10 seconds, or loading fails outright.
+
+Playback returns to full quality only when measured throughput exceeds the original bitrate by 50%, checked continuously while playing the low quality stream and again at each new hour. The asymmetric thresholds (1.1x to drop, 1.5x to return) keep a borderline connection from switching back and forth.
+
+The most recent measurement is remembered on the device: a connection last measured below the original bitrate (or below 3 Mbps when the bitrate is not yet known) starts playback on the low quality stream so a first frame appears immediately, then upgrades within a few segments if the speed allows.
+
+The quality selector shows which stream Auto is currently playing and why. A browser with Data Saver enabled stays on the low quality stream, a browser that cannot decode the original stream's codec (for example H.265 without HEVC support) plays the low quality stream for that camera, and pinning `Original` or `Low` bypasses Auto entirely.
+
+### Sub stream output args
+
+By default the sub stream is recorded with the same [output args](/configuration/ffmpeg_presets#output-args-presets) as the main recording stream, so it inherits any customization made to `ffmpeg.output_args.record`. Setting `ffmpeg.output_args.record_sub` gives the sub stream its own args instead. Like all `ffmpeg` config, this can be set globally or per camera.
+
+The most common reason to set this is a pair of streams whose audio differs. Many cameras send AAC on the main stream but PCM on the sub stream, and PCM cannot be copied into an mp4 recording. Copying the main stream's audio avoids re-encoding audio that is already AAC, while the sub stream still needs to be transcoded:
+
+```yaml
+ffmpeg:
+  output_args:
+    # main stream audio is already AAC, so copy it
+    record: preset-record-generic-audio-copy
+    # sub stream audio is PCM, so transcode it to AAC
+    record_sub: preset-record-generic-audio-aac
+```
+
+Other reasons to set this are recording a sub stream whose codec needs a different preset than the main stream, such as `preset-record-mjpeg`, or forcing a matching audio sample rate across the two streams with manual args ending in `-c:a aac -ar 16000`.
+
+:::warning
+
+Avoid removing audio from only one of the two streams (for example with `-an`). When one stream has audio and the other does not, playback of time ranges that combine both qualities is silent, so stripping audio from the sub stream also silences the merged timeline.
+
+:::
+
+### Which stream do features use?
+
+As a general rule, features that read recordings prefer the main stream and fall back to the sub stream for time ranges where the main recordings have expired. Analytics features use only the main stream.
+
+| Feature                                                                                  | Stream used                                                                                                   |
+| ---------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- |
+| Recording playback (History and Review)                                                  | Both (main preferred with sub fallback by default), or exactly one stream when a quality is selected manually |
+| Tracking details and Explore clip playback                                               | Main, falling back to sub where the main recordings have expired                                              |
+| Exports and clip downloads                                                               | Main; sub is used when no main recordings remain in the range (streams are never mixed in one file)           |
+| Frames grabbed from a recording in History (download snapshot, submit frame to Frigate+) | Main preferred, sub fallback                                                                                  |
+| Audio extraction (e.g., transcription)                                                   | Main preferred, sub fallback                                                                                  |
+| Motion search                                                                            | Main only                                                                                                     |
+| Review timeline motion data                                                              | Main only                                                                                                     |
+| Storage usage statistics                                                                 | Both streams counted, and listed separately per camera                                                        |
+
+This table covers only features that read recordings from disk. Tracked object snapshots and thumbnails (the images shown in Explore and sent with notifications, and the images submitted to Frigate+ from a tracked object) are captured live from the `detect` stream as the object is tracked, never from recordings, so sub stream recording does not affect them.
+
+### Trade-offs
+
+- Recording a second stream increases overall storage use. The increase is typically small relative to the main recordings, since the low quality stream is much smaller.
+- The go2rtc transcode approach continuously encodes the low quality stream, which uses CPU or GPU resources. This cost only applies to the transcode path; recording the camera's native sub stream does not re-encode. See the [go2rtc hardware acceleration documentation](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#source-ffmpeg) for accelerating the transcode.
+- Many camera sub streams do not include audio. If the source stream has no audio, the low quality recordings will not have audio.
+- **Matching video codecs and audio settings between the two streams gives the smoothest playback.** When playback combines both qualities on one timeline (the default `Auto` behavior: for example original quality during events with low quality in between, or low quality history after the original recordings expire) and the streams use different video codecs or audio settings, for example H.265 on the main stream and H.264 on the sub stream, or 16 kHz audio on one and 8 kHz on the other, playback still works: Frigate inserts a decoder reset at each quality transition, which can cause a barely-perceptible pause there. Configuring both streams in the camera's firmware to use the same video codec, audio codec, and sample rate makes transitions fully seamless, and a mismatched audio sample rate can also be corrected with [sub stream output args](#sub-stream-output-args). If one stream has audio and the other does not, combined time ranges play **without audio**; selecting a single quality with the playback selector always keeps that stream's audio.
+
 ## Can I have "continuous" recordings, but only at certain times?
 
 Using Frigate UI, Home Assistant, or MQTT, cameras can be automated to only record in certain situations or at certain times.
@@ -291,7 +448,7 @@ For advanced use cases, the [custom export HTTP API](../integrations/api/export-
 POST /export/custom/{camera_name}/start/{start_time}/end/{end_time}
 ```
 
-The request body accepts `ffmpeg_input_args` and `ffmpeg_output_args` to control encoding, frame rate, filters, and other FFmpeg options. If neither is provided, Frigate defaults to time-lapse output settings (25x speed, 30 FPS) with audio removed (`-an`). When providing your own `ffmpeg_input_args`, include `-an` if you want audio stripped from the export.
+The request body accepts `ffmpeg_input_args` and `ffmpeg_output_args` to control encoding, frame rate, filters, and other FFmpeg options. If neither is provided, Frigate defaults to time-lapse output settings (25x speed, 30 FPS).
 
 The following example exports a time-lapse at 60x speed with 25 FPS:
 

docs/docs/configuration/restream.md

@@ -197,7 +197,7 @@ For cameras that support two-way talk, go2rtc will automatically establish an au
 To prevent this, you must configure two separate stream instances:
 
 1. One stream instance with `#backchannel=0` for Frigate's viewing, recording, and detection (prevents go2rtc from establishing the blocking backchannel)
-2. A second stream instance with no `#` parameters at all for two-way talk functionality (can be used by Frigate's WebRTC viewer or other applications)
+2. A second stream instance without `#backchannel=0` for two-way talk functionality (can be used by Frigate's WebRTC viewer or other applications)
 
 Configuration example:
 
@@ -215,15 +215,13 @@ In this configuration:
 - `front_door` stream is used by Frigate for viewing, recording, and detection. The `#backchannel=0` parameter prevents go2rtc from establishing the audio output backchannel, so it won't block two-way talk access.
 - `front_door_twoway` stream is used for two-way talk functionality. This stream can be used by Frigate's WebRTC viewer when two-way talk is enabled, or by other applications (like Home Assistant Advanced Camera Card) that need access to the camera's audio output channel.
 
-Any `#` parameter on a bare `rtsp://` source disables the backchannel unless the URL explicitly contains `#backchannel=1`. A two-way talk stream with something like `#video=h264` on it silently loses two-way audio, and Frigate will report that two-way talk is unavailable for that stream.
-
 ## Security: Restricted Stream Sources
 
 For security reasons, the `echo:`, `expr:`, and `exec:` stream sources are disabled by default in go2rtc. These sources allow arbitrary command execution and can pose security risks if misconfigured.
 
 If you attempt to use these sources in your configuration, the streams will be removed and an error message will be printed in the logs.
 
-To enable these sources, you must set the environment variable `GO2RTC_ALLOW_ARBITRARY_EXEC=true`. This can be done in your Docker Compose file or container environment:
+To enable these sources, you must set the environment variable `GO2RTC_ALLOW_ARBITRARY_EXEC=true`. This can be done in your Docker Compose file or container environment, or for Home Assistant App users with the `go2rtc_allow_arbitrary_exec` option in the App's configuration. The `environment_vars` section of the Frigate config can't enable it:
 
 ```yaml
 environment:

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"). Frigate derives the trigger's
-     internal **ID** from this name, which can be revealed and edited with the show/hide toggle.
+   - Enter a **Name** for the trigger (e.g., "Red Car Alert").
+   - Enter a descriptive **Friendly Name** for the trigger (e.g., "Red car on the driveway camera").
    - 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 **Name**, **Objects**, **Loitering Time**, and **Inertia** in the zone editor.
+4. Configure zone options such as **Friendly 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, 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.
+3. In the zone editor, enter the real-world **Distances** 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. Set with `min_score` in the config, shown as **Minimum confidence** in the settings UI.
+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.
 
 ## 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. Set with `threshold` in the config, shown as **Confidence threshold** in the settings UI.
+The median score an object must reach to be considered a true positive.
 
 ## Top Score
 

docs/docs/frigate/installation.md

@@ -612,6 +612,8 @@ Home Assistant OS users can install via the App repository.
 5. Start the App
 6. Use the _Open Web UI_ button to access the Frigate UI, then click in the _cog icon_ > _Configuration editor_ and configure Frigate to your liking
 
+App users who can't set container environment variables can put `FRIGATE_` values in a `secrets.yaml` in the config directory instead. See [`secrets.yaml`](../configuration/advanced/system.md#secretsyaml).
+
 There are several variants of the App available:
 
 | App Variant                | Description                                                |

docs/docs/guides/getting_started.md

@@ -204,8 +204,8 @@ You need to refer to **Configure hardware acceleration** above to enable the con
 <ConfigTabs>
 <TabItem value="ui">
 
-1. Navigate to <NavPath path="Settings > System > Detectors and model" /> and add a detector with **Type** `OpenVINO` and **Device** `GPU`
-2. On the same page, in the **Custom Model** tab, configure the model settings for OpenVINO:
+1. Navigate to <NavPath path="Settings > System > Detection models" /> and select **Intel GPU** from the **Hardware** dropdown
+2. On the same model, open the **Custom Model** tab and configure the model settings for OpenVINO:
 
 | Field                                    | Value                                      |
 | ---------------------------------------- | ------------------------------------------ |
@@ -222,15 +222,12 @@ You need to refer to **Configure hardware acceleration** above to enable the con
 ```yaml {3-6,9-15,20-21}
 mqtt: ...
 
-detectors: # <---- add detectors
-  ov:
-    type: openvino  # <---- use openvino detector
-    device: GPU
-
-# We will use the default MobileNet_v2 model from OpenVINO.
-model:
-  width: 300
-  height: 300
+models: # <---- add models
+  - devices:
+      - openvino:GPU # <---- use the openvino detector on the GPU
+    # We will use the default MobileNet_v2 model from OpenVINO.
+    width: 300
+    height: 300
   input_tensor: nhwc
   input_pixel_format: bgr
   path: /openvino-model/ssdlite_mobilenet_v2.xml
@@ -273,7 +270,7 @@ services:
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detectors and model" /> and add a detector with **Type** `EdgeTPU` and **Device** `usb`.
+Navigate to <NavPath path="Settings > System > Detection models" /> and select **Coral EdgeTPU (USB)** from the **Hardware** dropdown.
 
 </TabItem>
 <TabItem value="yaml">
@@ -281,10 +278,9 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and add a
 ```yaml {3-6,11-12}
 mqtt: ...
 
-detectors: # <---- add detectors
-  coral:
-    type: edgetpu
-    device: usb
+models: # <---- add models
+  - devices:
+      - edgetpu:usb
 
 cameras:
   name_of_your_camera:
@@ -321,10 +317,9 @@ If you are using YAML to configure Frigate instead of the UI, your configuration
 mqtt:
   enabled: False
 
-detectors:
-  coral:
-    type: edgetpu
-    device: usb
+models:
+  - devices:
+      - edgetpu:usb
 
 cameras:
   name_of_your_camera:
@@ -357,7 +352,7 @@ In order to review activity in the Frigate UI, recordings need to be enabled.
 ```yaml {16-17}
 mqtt: ...
 
-detectors: ...
+models: ...
 
 cameras:
   name_of_your_camera:

docs/docs/integrations/mqtt.md

@@ -11,12 +11,6 @@ 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`
@@ -559,22 +553,25 @@ must be enabled in the configuration.
 
 Topic with current state of Birdseye for a camera. Published values are `ON` and `OFF`.
 
-### `frigate/<camera_name>/birdseye_mode/set`
+### `frigate/<camera_name>/birdseye_modes/set`
 
-Topic to set Birdseye mode for a camera. Birdseye offers different modes to customize under which circumstances the camera is shown.
+Topic to set the Birdseye activity types for a camera. Send one uppercase activity type or combine multiple types with commas, for example `MOTION,ALERTS`.
 
-_Note: Changing the value from `CONTINUOUS` -> `MOTION | OBJECTS` will take up to 30 seconds for
+_Note: Changing the value from `CONTINUOUS` to non-continuous activity types will take up to 30 seconds for
 the camera to be removed from the view._
 
-| Command      | Description                                                       |
-| ------------ | ----------------------------------------------------------------- |
-| `CONTINUOUS` | Always included                                                   |
-| `MOTION`     | Show when detected motion within the last 30 seconds are included |
-| `OBJECTS`    | Shown if an active object tracked within the last 30 seconds      |
+| Command       | Description                                                      |
+| ------------- | ---------------------------------------------------------------- |
+| `CONTINUOUS`  | Always included                                                  |
+| `MOTION`      | Shown if motion was detected within the last 30 seconds          |
+| `ALL_OBJECTS` | Shown if a tracked object was present within the last 30 seconds |
+| `ALERTS`      | Shown while an alert review item is in progress                  |
+| `DETECTIONS`  | Shown while a detection review item is in progress               |
+| `NONE`        | Never included                                                   |
 
-### `frigate/<camera_name>/birdseye_mode/state`
+### `frigate/<camera_name>/birdseye_modes/state`
 
-Topic with current state of the Birdseye mode for a camera. Published values are `CONTINUOUS`, `MOTION`, `OBJECTS`.
+Topic with the current Birdseye activity types for a camera. Multiple enabled types are published as a comma-separated value in the order `CONTINUOUS`, `MOTION`, `ALL_OBJECTS`, `ALERTS`, `DETECTIONS`. `NONE` is published when no activity types are enabled.
 
 ### `frigate/<camera_name>/notifications/set`
 

docs/docs/integrations/plus.md

@@ -59,13 +59,12 @@ You can view all of your submitted images at [https://plus.frigate.video](https:
 
 Once you have [requested your first model](../plus/first_model.md) and gotten your own model ID, it can be used with a special model path. No other information needs to be configured for Frigate+ models because it fetches the remaining config from Frigate+ automatically.
 
-You can either choose the new model from the <NavPath path="Settings > System > Detectors and model" /> pane in the Frigate UI (the **Frigate+ Model** tab), or manually set the model at the root level in your config:
+You can either choose the new model from the <NavPath path="Settings > System > Detection models" /> pane in the Frigate UI (on the **Frigate+** tab of the model you want to change), or set it on that model in your config:
 
 ```yaml
-detectors: ...
-
-model:
-  path: plus://<your_model_id>
+models:
+  - devices: ...
+    path: plus://<your_model_id>
 ```
 
 :::note
@@ -79,10 +78,11 @@ Models are downloaded into the `/config/model_cache` folder and only downloaded
 If needed, you can override the labelmap for Frigate+ models. This is not recommended as renaming labels will break the Submit to Frigate+ feature if the labels are not available in Frigate+.
 
 ```yaml
-model:
-  path: plus://<your_model_id>
-  labelmap:
-    3: animal
-    4: animal
-    5: animal
+models:
+  - devices: ...
+    path: plus://<your_model_id>
+    labelmap:
+      3: animal
+      4: animal
+      5: animal
 ```

docs/docs/plus/faq.md

@@ -21,13 +21,7 @@ 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 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.
+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.
 
 ### Why can't I submit images to Frigate+?
 

docs/docs/plus/first_model.md

@@ -30,16 +30,15 @@ Models available in Frigate+ can be used with a special model path. No other inf
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detectors and model" />. In the **Detection Model** section, choose the **Frigate+** tab. Select your new Frigate+ model from the **Available Frigate+ models** dropdown, then click **Save**. Restart Frigate to apply the change.
+Navigate to <NavPath path="Settings > System > Detection models" />. On the model you want to change, choose the **Frigate+** tab and select your new Frigate+ model from the **Available Frigate+ models** dropdown, then click **Save**. Restart Frigate to apply the change.
 
 </TabItem>
 <TabItem value="yaml">
 
 ```yaml
-detectors: ...
-
-model:
-  path: plus://<your_model_id>
+models:
+  - devices: ...
+    path: plus://<your_model_id>
 ```
 
 :::tip
@@ -64,20 +63,20 @@ 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 **Minimum confidence** and **Confidence threshold** for each object type, then click **Save**.
+Navigate to <NavPath path="Settings > Global configuration > Objects" />. Under **Object filters**, set **Min Score** and **Threshold** for each object type, then click **Save**.
 
-| Object            | Minimum confidence | Confidence threshold |
-| ----------------- | ------------------ | -------------------- |
-| **dog**           | .7                 | .9                   |
-| **cat**           | .65                | .8                   |
-| **face**          | .7                 |                      |
-| **package**       | .65                | .9                   |
-| **license_plate** | .6                 |                      |
-| **amazon**        | .75                |                      |
-| **ups**           | .75                |                      |
-| **fedex**         | .75                |                      |
-| **person**        | .65                | .85                  |
-| **car**           | .65                | .85                  |
+| Object            | Min Score | Threshold |
+| ----------------- | --------- | --------- |
+| **dog**           | .7        | .9        |
+| **cat**           | .65       | .8        |
+| **face**          | .7        |           |
+| **package**       | .65       | .9        |
+| **license_plate** | .6        |           |
+| **amazon**        | .75       |           |
+| **ups**           | .75       |           |
+| **fedex**         | .75       |           |
+| **person**        | .65       | .85       |
+| **car**           | .65       | .85       |
 
 </TabItem>
 <TabItem value="yaml">

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`, `baby`
-- **Vehicles**: `car`, `motorcycle`, `bicycle`, `boat`, `school_bus`, `garbage truck`, `license_plate`
+- **People**: `person`, `face`
+- **Vehicles**: `car`, `motorcycle`, `bicycle`, `boat`, `school_bus`, `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`, `possum`, `rodent`
-- **Other**: `package`, `waste_bin`, `bbq_grill`, `robot_lawnmower`, `umbrella`, `baby_stroller`
+- **Animals**: `dog`, `cat`, `deer`, `horse`, `bird`, `raccoon`, `fox`, `bear`, `cow`, `squirrel`, `goat`, `rabbit`, `skunk`, `kangaroo`
+- **Other**: `package`, `waste_bin`, `bbq_grill`, `robot_lawnmower`, `umbrella`
 
 Other object types available in the default Frigate model are not available. Additional object types will be added in future releases.
 
@@ -77,12 +77,9 @@ 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 `duck` labels are mapped to `bird` until support for new labels is added.
+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.
 
-- **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`
+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`
 
 Candidate labels are not available for automatic suggestions.
 

docs/docs/troubleshooting/common_errors.md

@@ -66,19 +66,17 @@ An FFmpeg message meaning it probed the stream but never saw enough decodable vi
 
 ## Recording
 
-<FaqItem id="no-new-recording-segments" question="No new recording segments were created (or: No new valid recording segments were created / No valid segments created since last invalid segment) for <camera> in the last 120s">
+<FaqItem id="no-new-recording-segments" question="No new recording segments were created for <camera> in the last 120s">
 
-Frigate's record watchdog is restarting the record FFmpeg process because the camera stopped producing usable recordings. The wording distinguishes the cases: `No new recording segments` means no new segment file reached the cache, so ffmpeg isn't getting video out of the record stream; the two `valid` variants mean recordings are arriving but keep failing validation. Either way the fault is on the camera or network side, and the restart is Frigate trying to recover.
+Frigate's record watchdog is restarting the record FFmpeg process because no valid segment has reached the cache. This means the record stream is not connecting or the segments are being rejected (see the audio-codec entry below).
 
-See [Recordings: no new recording segments were created](/troubleshooting/recordings#no-new-recording-segments-were-created).
+See [Recordings: the record stream isn't connecting](/troubleshooting/recordings#the-record-stream-isnt-connecting).
 
 </FaqItem>
 
-<FaqItem id="invalid-or-missing-video-stream-in-segment" question="Invalid or missing video stream in segment. Discarding. / Discarding a corrupt recording segment / Failed to probe corrupt segment / Invalid recording segment detected">
+<FaqItem id="invalid-or-missing-video-stream-in-segment" question="Invalid or missing video stream in segment. Discarding.">
 
-A cached recording segment failed validation and was deleted, either because it had no readable video stream or because its length was impossible. This nearly always means the camera stopped sending usable video partway through the segment: a camera that rebooted, dropped the connection, or ran out of simultaneous connections, or an unreliable link such as WiFi or a failing switch port. Broken camera timestamps (a "Smart Codec" / H.264+ mode) cause the corrupt-segment variants. The same stream failure trips the record watchdog, so the restarts above usually appear alongside these messages.
-
-See [Recordings: invalid or missing video stream in segment](/troubleshooting/recordings#invalid-or-missing-video-stream-in-segment).
+A cached recording segment failed validation (no readable video stream) and was deleted. The most common cause is a segment that was truncated because the record FFmpeg process was killed mid-write, so this often appears alongside, and as a consequence of, the record-stream restarts above. A segment containing only audio triggers it too.
 
 </FaqItem>
 
@@ -133,7 +131,7 @@ The process was killed by the CPU for executing an unsupported instruction. Ther
 
 <FaqItem id="onnx-invalidprotobuf" question="ONNX Runtime InvalidProtobuf / failed to load model">
 
-ONNX Runtime could not parse the model file. The file exists but its contents are not a valid ONNX model, usually a corrupted or interrupted download in `model_cache`, or the wrong file pointed at by `model.path`. Delete the cached model file so Frigate re-downloads it, and confirm `model.path` points at an actual `.onnx` model. See [ONNX detector configuration](/configuration/object_detectors#onnx).
+ONNX Runtime could not parse the model file. The file exists but its contents are not a valid ONNX model, usually a corrupted or interrupted download in `model_cache`, or the wrong file pointed at by a model's `path`. Delete the cached model file so Frigate re-downloads it, and confirm the model's `path` points at an actual `.onnx` model. See [ONNX detector configuration](/configuration/object_detectors#onnx).
 
 </FaqItem>
 

docs/docs/troubleshooting/faqs.md

@@ -39,20 +39,6 @@ To do this efficiently the following setup is required:
 
 When this is done correctly, the GPU will do the decoding and scaling which will result in a small increase in CPU usage but with better results.
 
-### How can I rotate my camera's video feed?
-
-Rotation is best done in the camera's firmware settings (usually called rotate, flip, or corridor mode) so the video arrives already rotated and no extra processing is needed. Check there first.
-
-If your camera does not support rotation, go2rtc's ffmpeg module can rotate the stream with the `#rotate` parameter (`90`, `180`, `270`, or `-90`), but this is not recommended: rotation requires transcoding (re-encoding) the video, which significantly increases CPU usage, especially for high resolution streams.
-
-```yaml
-go2rtc:
-  streams:
-    my_camera: "ffmpeg:rtsp://user:password@192.168.1.10:554/stream#video=h264#hardware#rotate=90"
-```
-
-Point the camera's inputs at the restream as described in the [restream docs](/configuration/restream.md), and swap `detect -> width` and `detect -> height` to match the rotated resolution.
-
 ### My mjpeg stream or snapshots look green and crazy
 
 This almost always means that the width/height defined for your camera are not correct. Double check the resolution with VLC or another player. Also make sure you don't have the width and height values backwards.

docs/docs/troubleshooting/go2rtc.md

@@ -78,9 +78,7 @@ go2rtc:
 
 :::warning
 
-The transcoding modifiers (`#video=`, `#audio=`, `#hardware`, …) **only take effect on a source that is prefixed with `ffmpeg:`**. Adding them to a bare `rtsp://…#audio=opus` source does nothing: go2rtc ignores them. Likewise, when a source references another stream by name (e.g. `ffmpeg:back#audio=aac`), the name must match the stream key **exactly** (it is case sensitive), or the transcode is silently never produced. This is the single most common configuration mistake. In the Frigate UI, the **Use compatibility mode (ffmpeg)** toggle adds the `ffmpeg:` prefix for you.
-
-A bare `rtsp://` source reads a different set of modifiers: `#backchannel=`, `#media=`, `#timeout=`, and `#transport=`. These do nothing on an `ffmpeg:` source. Adding **any** modifier to a bare `rtsp://` source also disables the camera's backchannel unless the URL explicitly contains `#backchannel=1`, so a stream dedicated to two-way talk should carry no modifiers at all.
+The `#`-modifiers (`#video=`, `#audio=`, `#hardware`, `#backchannel=0`, …) **only take effect on a source that is prefixed with `ffmpeg:`**. Adding them to a bare `rtsp://…#audio=opus` source does nothing: go2rtc ignores them. Likewise, when a source references another stream by name (e.g. `ffmpeg:back#audio=aac`), the name must match the stream key **exactly** (it is case sensitive), or the transcode is silently never produced. This is the single most common configuration mistake. In the Frigate UI, the **Use compatibility mode (ffmpeg)** toggle adds the `ffmpeg:` prefix for you.
 
 :::
 
@@ -155,7 +153,7 @@ WebRTC is only attempted when MSE fails or when using a camera's two-way talk fe
 
 - **Codec mismatch**: WebRTC cannot carry H.265 or AAC. The stream backing the WebRTC view must provide Opus (or PCMA/PCMU) audio and H.264 video. Add an `ffmpeg:back#audio=opus` source as shown above.
 - **Port `8555` not reachable, or no candidates set**: WebRTC needs port `8555` (both TCP and UDP) open and a reachable candidate advertised. On Docker installs running on a custom/overlay network, go2rtc may advertise unreachable container IPs as ICE candidates; setting `webrtc.filters.candidates: []` and supplying only your host's LAN IP resolves this. See [WebRTC extra configuration](/configuration/live#webrtc-extra-configuration).
-- **Two-way talk** additionally requires a secure context (HTTPS or the authenticated port `8971`, because browsers block microphone access on plain HTTP). The camera's RTSP backchannel must also be handled correctly: go2rtc seizes the backchannel by default, which blocks two-way audio for other consumers and can inject static. Disable it on the primary stream with `#backchannel=0` and use a separate dedicated stream for talk, carrying no `#` modifiers of any kind, as documented in [preventing go2rtc from blocking two-way audio](/configuration/restream#two-way-talk-restream).
+- **Two-way talk** additionally requires a secure context (HTTPS or the authenticated port `8971`, because browsers block microphone access on plain HTTP). The camera's RTSP backchannel must also be handled correctly: go2rtc seizes the backchannel by default, which blocks two-way audio for other consumers and can inject static. Disable it on the primary stream with `#backchannel=0` and use a separate dedicated stream for talk, as documented in [preventing go2rtc from blocking two-way audio](/configuration/restream#two-way-talk-restream).
 
 ## High CPU usage
 

docs/docs/troubleshooting/recordings.md

@@ -209,50 +209,6 @@ If the record stream uses a "Smart Codec"/H.264+ mode or changes encoding parame
 
 </FaqItem>
 
-<FaqItem id="invalid-or-missing-video-stream-in-segment" question="I see the message: WARNING : Invalid or missing video stream in segment ... Discarding.">
-
-Every recording segment is validated before it leaves the cache. Frigate probes each finished `.mp4` in `/tmp/cache` and requires a readable video stream and a valid duration before moving to storage. A segment that fails is deleted, so those ~10 seconds of footage are lost. Three messages come from this check:
-
-- `Invalid or missing video stream in segment <path>. Discarding.` The segment holds no video, or could not be read at all.
-- `Failed to probe corrupt segment <path>` followed by `Discarding a corrupt recording segment: <path>`. The segment was read, but its length could not be determined.
-- `Discarding a corrupt recording segment: <path>` on its own. The segment's length is impossible (empty, or longer than ten minutes), which points at broken timestamps coming from the camera.
-
-For each one, the camera watchdog also logs `Invalid recording segment detected for <camera> at <timestamp>`.
-
-:::warning
-
-This is almost always a **camera or network problem**, not a Frigate one. A segment is only complete once ffmpeg has finished writing it, so anything that interrupts the stream partway through leaves behind a file that cannot be saved. Frigate is reporting the interruption, not causing it.
-
-:::
-
-#### Start with the camera and the network
-
-- **The camera dropped the connection.** Cameras reboot, reinitialize their stream when switching to night mode, and cut clients off when they are overloaded or out of simultaneous connections. Count everything pulling from the camera at once: Frigate's detect and record streams, go2rtc, a phone app, and any other NVR each use one. Routing all roles through a single [RTSP restream](/configuration/restream#reduce-connections-to-camera) so the camera only ever sees one connection often resolves this by itself.
-- **The link to the camera is unreliable.** WiFi cameras, powerline adapters, a saturated uplink, a failing switch port, or a marginal cable all produce this pattern, and usually only on one camera at a time. WiFi cameras are [not recommended](https://ipcamtalk.com/threads/multiple-cameras-high-bandwidth.77100/#post-861110).
-- **The camera cannot reliably send what it is being asked for.** A high bitrate 4K stream can be more than the camera's own hardware can encode and push out under load. Lower the bitrate, or record a lower-resolution profile.
-- **The camera is using a "Smart Codec", H.264+, or H.265+ mode.** These change encoding parameters mid-stream and produce the broken timestamps behind the corrupt-segment variant. Turn the mode off and set the camera's keyframe interval equal to its frame rate. See [Segments are only ~1 second long](#segments-are-only-1-second-long).
-
-Read the rest of the Frigate and/or go2rtc log around the **first** occurrence. When the camera or the network is at fault, other messages show up with it, such as `No frames received from <camera> in 20 seconds`, `Non-monotonic DTS`, `RTP: PT=xx: bad cseq`, `error while decoding MB`, or a connection timeout. Each of those is explained in [Common error messages](/troubleshooting/common_errors). To confirm the camera is the source, open its stream in the [go2rtc web interface](/troubleshooting/go2rtc) on port `1984` or play the same URL in VLC, and leave it running long enough for the failures to happen again.
-
-#### If the camera and network check out
-
-- **Audio the recording cannot store.** Some cameras send G.711 audio, which cannot be saved in an MP4 and stops segments from finalizing. See [Incompatible audio codec](#incompatible-audio-codec-recordings-silently-fail-to-save).
-- **Frigate itself was stopped or restarted.** A single warning per camera around a restart is expected and needs no action.
-- **The system ran out of room or memory.** A full `/tmp/cache`, or the host killing Frigate for using too much memory, cuts off the segment being written. Both leave other errors in the log alongside this one. See [No space left on device](#errno-28-no-space-left-on-device).
-
-</FaqItem>
-
-<FaqItem id="no-new-recording-segments-were-created" question="I see the message: ERROR : No new recording segments were created for <camera> in the last 120s. Restarting the ffmpeg record process...">
-
-When a camera stops producing usable recordings for two minutes, Frigate restarts that camera's record process to try to recover. The wording tells you how far the recordings got:
-
-- **`No new recording segments were created`**: no new segment file showed up in the cache at all, so ffmpeg isn't getting video out of the record stream. The camera is unreachable or refusing the connection, the stream URL, path, or credentials are wrong, or the camera accepted the connection and then sent nothing. See [The record stream isn't connecting](#the-record-stream-isnt-connecting).
-- **`No new valid recording segments were created`** and **`No valid segments created since last invalid segment`**: recordings are arriving, but they keep failing validation, so the camera is sending video that cannot be saved. See [Invalid or missing video stream in segment](#invalid-or-missing-video-stream-in-segment) above.
-
-The restart is Frigate recovering from a problem, not causing one. One of these after a camera reboot or a brief network drop is normal. Seeing them repeat every couple of minutes means the camera or the network is still failing, and the restarts can extend the damage, because each one cuts off the segment that was being written. Work from the earliest failure in that camera's log rather than from the restarts.
-
-</FaqItem>
-
 <FaqItem id="i-see-the-message-warning--unable-to-keep-up-with-recording-segments-in-cache-for-camera-keeping-the-5-most-recent-segments-out-of-6-and-discarding-the-rest" question="I see the message: WARNING : Unable to keep up with recording segments in cache for camera. Keeping the 5 most recent segments out of 6 and discarding the rest...">
 
 This warning means the recording maintainer cannot move recording segments from the RAM cache to disk fast enough. When the cache fills up, Frigate discards the oldest segments to avoid running out of memory and crashing, so you lose recorded footage. This is almost always a storage throughput or system resource problem. Work through the steps below to identify which.
@@ -397,11 +353,19 @@ 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 go2rtc stream configuration
+#### Step 6: Verify hardware acceleration 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 7: Check system resources
+#### Step 8: Check system resources
 
 If none of the above apply, the issue may be a general resource constraint. Monitor the following on your host:
 

docs/docs/usage/live.md

@@ -40,7 +40,7 @@ Deleting a group also clears any custom layout you saved for it.
 
 ## Rearranging a camera group layout
 
-On desktop and tablet, each camera group has its own freely-arrangeable grid. Enter **Edit Layout** mode from the layout button in the lower-right corner: camera tiles gain a drag handle and corner resize handles. Drag a tile to reposition it and drag a corner to resize it (the aspect ratio is preserved). Exit edit mode to save. The layout is stored in your browser per device, so each device can have its own arrangement.
+On desktop and tablet, each camera group has its own freely-arrangeable grid. Enter **Edit Layout** mode from the layout button in the lower-right corner: camera tiles gain a drag handle and corner resize handles. Drag a tile to reposition it and drag a corner to resize it (the aspect ratio is preserved). Exit edit mode to save. The layout is stored in your browser per device, so each device can have its own arrangement, and layouts can be exported to a file and imported on another device.
 
 The default **All Cameras** dashboard is not manually arrangeable. It automatically sizes tiles based on each camera's aspect ratio (wide cameras span two columns, tall cameras span two rows).
 
@@ -68,7 +68,7 @@ For non-default groups, the context menu also exposes **Streaming Settings** for
 - the **streaming method**: **No Streaming**, **Smart Streaming** (recommended), or **Continuous Streaming** (higher bandwidth), and
 - **compatibility mode**, for devices that have trouble rendering the default player.
 
-These settings are saved per group and per device in your browser, not in your config file.
+These settings are saved per group and per device in your browser, not in your config file, and can be exported to a file and imported on another device.
 
 ## The single-camera view
 

docs/docusaurus.config.ts

@@ -3,9 +3,6 @@ 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",
@@ -26,17 +23,17 @@ const config: Config = {
     mermaid: true,
   },
   i18n: {
-    defaultLocale: "en",
-    locales: ["en"],
+    defaultLocale: '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.
@@ -48,8 +45,8 @@ const config: Config = {
             50%       { transform: scale(1.1); }
           }
         </style>`,
-      backgroundColor: "#005f73",
-      textColor: "#e0fbfc",
+      backgroundColor: '#005f73',
+      textColor: '#e0fbfc',
       isCloseable: false,
     },
     docs: {
@@ -86,15 +83,15 @@ const config: Config = {
       },
     },
     prism: {
-      magicComments: [
+      magicComments:[
         {
-          className: "theme-code-block-highlighted-line",
-          line: "highlight-next-line",
-          block: { start: "highlight-start", end: "highlight-end" },
+          className: 'theme-code-block-highlighted-line',
+          line: 'highlight-next-line',
+          block: {start: 'highlight-start', end: 'highlight-end'},
         },
         {
-          className: "code-block-error-line",
-          line: "highlight-error-line",
+          className: 'code-block-error-line',
+          line: 'highlight-error-line',
         },
       ],
       additionalLanguages: ["bash", "json"],
@@ -134,11 +131,6 @@ 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",
@@ -156,19 +148,19 @@ const config: Config = {
           position: "right",
         },
         {
-          type: "localeDropdown",
-          position: "right",
+          type: 'localeDropdown',
+          position: 'right',
           dropdownItemsAfter: [
             {
-              label: "简体中文（社区翻译）",
-              href: "https://docs.frigate-cn.video",
-            },
-          ],
+              label: '简体中文（社区翻译）',
+              href: 'https://docs.frigate-cn.video',
+            }
+          ]
         },
         {
-          href: "https://github.com/blakeblackshear/frigate",
-          label: "GitHub",
-          position: "right",
+          href: 'https://github.com/blakeblackshear/frigate',
+          label: 'GitHub',
+          position: 'right',
         },
       ],
     },

docs/scripts/lib/nav_map.py

@@ -63,8 +63,7 @@ SYSTEM_NAV: dict[str, tuple[str, str]] = {
     "environment_vars": ("System", "Environment variables"),
     "telemetry": ("System", "Telemetry"),
     "birdseye": ("System", "Birdseye"),
-    "detectors": ("System", "Detectors and model"),
-    "model": ("System", "Detectors and model"),
+    "models": ("System", "Detection models"),
 }
 
 # All known top-level config section keys

docs/static/frigate-api.yaml

@@ -713,7 +713,7 @@ paths:
         | `improve_contrast` | `ON`, `OFF` |
         | `ptz_autotracker` | `ON`, `OFF` |
         | `birdseye` | `ON`, `OFF` |
-        | `birdseye_mode` | `CONTINUOUS`, `MOTION`, `OBJECTS` |
+        | `birdseye_modes` | `CONTINUOUS`, `MOTION`, `ALL_OBJECTS`, `ALERTS`, `DETECTIONS`, `NONE`, or a comma-separated combination |
         | `motion_contour_area` | integer |
         | `motion_threshold` | integer |
         | `motion_mask` | `ON`, `OFF` |
@@ -803,7 +803,7 @@ paths:
         | `improve_contrast` | `ON`, `OFF` |
         | `ptz_autotracker` | `ON`, `OFF` |
         | `birdseye` | `ON`, `OFF` |
-        | `birdseye_mode` | `CONTINUOUS`, `MOTION`, `OBJECTS` |
+        | `birdseye_modes` | `CONTINUOUS`, `MOTION`, `ALL_OBJECTS`, `ALERTS`, `DETECTIONS`, `NONE`, or a comma-separated combination |
         | `motion_contour_area` | integer |
         | `motion_threshold` | integer |
         | `motion_mask` | `ON`, `OFF` |
@@ -1476,7 +1476,7 @@ paths:
         - Classification
       summary: Get custom classification attributes
       description: |-
-        **Access:** Authenticated user with access to all cameras.
+        **Access:** Admin role required.
 
         Returns custom classification attributes for a given object type.
             Only includes models with classification_type set to 'attribute'.
@@ -1510,8 +1510,8 @@ paths:
               schema:
                 $ref: '#/components/schemas/HTTPValidationError'
       security:
-        - frigateUserAuth: []
-      x-required-role: all_cameras
+        - frigateAdminAuth: []
+      x-required-role: admin
   /classification/{name}/train:
     get:
       tags:
@@ -2308,15 +2308,15 @@ paths:
                 $ref: '#/components/schemas/HTTPValidationError'
       security:
         - frigateUserAuth: []
-      x-required-role: camera
-      description: '**Access:** Authenticated user with access to the referenced camera.'
+      x-required-role: any
+      description: '**Access:** Any authenticated user.'
   /review/summarize/start/{start_ts}/end/{end_ts}:
     post:
       tags:
         - Review
       summary: Generate Review Summary
       description: |-
-        **Access:** Authenticated user with access to all cameras.
+        **Access:** Admin role required.
 
         Use GenAI to summarize review items over a period of time.
       operationId: 
@@ -2347,8 +2347,8 @@ paths:
               schema:
                 $ref: '#/components/schemas/HTTPValidationError'
       security:
-        - frigateUserAuth: []
-      x-required-role: all_cameras
+        - frigateAdminAuth: []
+      x-required-role: admin
   /:
     get:
       tags:
@@ -2946,6 +2946,44 @@ paths:
         - frigateUserAuth: []
       x-required-role: any
       description: '**Access:** Any authenticated user.'
+  /categorized_object_names:
+    get:
+      tags:
+        - App
+      summary: Get known object names by object type
+      description: |-
+        **Access:** Any authenticated user.
+
+        Returns the sub labels and attributes this install can attach,
+            grouped by object type. Unlike /sub_labels, which reflects what has already been
+            detected, this reads the config and model files, so it covers recognized face
+            names, named license plates, custom object classification categories, and the
+            detector attributes of tracked objects.
+      operationId: categorized_object_names_categorized_object_names_get
+      parameters:
+        - name: object_type
+          in: query
+          required: false
+          schema:
+            anyOf:
+              - type: string
+              - type: 'null'
+            title: Object Type
+      responses:
+        '200':
+          description: Successful Response
+          content:
+            application/json:
+              schema: {}
+        '422':
+          description: Validation Error
+          content:
+            application/json:
+              schema:
+                $ref: '#/components/schemas/HTTPValidationError'
+      security:
+        - frigateUserAuth: []
+      x-required-role: any
   /audio_labels:
     get:
       tags:
@@ -3972,6 +4010,49 @@ paths:
       security:
         - frigateAdminAuth: []
       x-required-role: admin
+  /hardware/probe:
+    get:
+      tags:
+        - Hardware
+      summary: Probe Hardware
+      description: |-
+        **Access:** Admin role required.
+
+        Get the object detection hardware attached to this system.
+
+        Args:
+            refresh: Probe again instead of returning the cached result
+
+        Returns:
+            Every kind of detection hardware that was found
+      operationId: probe_hardware_hardware_probe_get
+      parameters:
+        - name: refresh
+          in: query
+          required: false
+          schema:
+            type: boolean
+            default: false
+            title: Refresh
+      responses:
+        '200':
+          description: Successful Response
+          content:
+            application/json:
+              schema:
+                type: array
+                items:
+                  $ref: '#/components/schemas/DetectionHardware'
+                title: Response Probe Hardware Hardware Probe Get
+        '422':
+          description: Validation Error
+          content:
+            application/json:
+              schema:
+                $ref: '#/components/schemas/HTTPValidationError'
+      security:
+        - frigateAdminAuth: []
+      x-required-role: admin
   /events:
     get:
       tags:
@@ -4073,16 +4154,6 @@ 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
@@ -4388,16 +4459,6 @@ 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
@@ -6004,6 +6065,65 @@ paths:
       security:
         - frigateUserAuth: []
       x-required-role: camera
+  /vod/{camera_name}/{stream}/start/{start_ts}/end/{end_ts}:
+    get:
+      tags:
+        - Media
+      summary: Vod Ts Stream
+      description: |-
+        **Access:** Authenticated user with access to the referenced camera.
+
+        Returns an HLS playlist pinned to one stream type (main or sub) for the specified timestamp-range on the specified camera. Append /master.m3u8 or /index.m3u8 for HLS playback.
+      operationId: 
+        vod_ts_stream_vod__camera_name___stream__start__start_ts__end__end_ts__get
+      parameters:
+        - name: camera_name
+          in: path
+          required: true
+          schema:
+            anyOf:
+              - type: string
+              - type: 'null'
+            title: Camera Name
+        - name: stream
+          in: path
+          required: true
+          schema:
+            $ref: '#/components/schemas/VodStreamPreference'
+        - name: start_ts
+          in: path
+          required: true
+          schema:
+            type: number
+            title: Start Ts
+        - name: end_ts
+          in: path
+          required: true
+          schema:
+            type: number
+            title: End Ts
+        - name: force_discontinuity
+          in: query
+          required: false
+          schema:
+            type: boolean
+            default: false
+            title: Force Discontinuity
+      responses:
+        '200':
+          description: Successful Response
+          content:
+            application/json:
+              schema: {}
+        '422':
+          description: Validation Error
+          content:
+            application/json:
+              schema:
+                $ref: '#/components/schemas/HTTPValidationError'
+      security:
+        - frigateUserAuth: []
+      x-required-role: camera
   /events/{event_id}/snapshot.jpg:
     get:
       tags:
@@ -6942,6 +7062,63 @@ paths:
       security:
         - frigateUserAuth: []
       x-required-role: camera
+  /{camera_name}/recordings/coverage:
+    get:
+      tags:
+        - Recordings
+      summary: Recordings Coverage
+      description: |-
+        **Access:** Authenticated user with access to the referenced camera.
+
+        Returns merged recording coverage spans plus codec compatibility.
+
+        codecs_compatible is false only when more than one known video codec
+        appears across the range's rows, the case where the merged vod route
+        degrades to a single-stream manifest.
+      operationId: recordings_coverage__camera_name__recordings_coverage_get
+      parameters:
+        - name: camera_name
+          in: path
+          required: true
+          schema:
+            anyOf:
+              - type: string
+              - type: 'null'
+            title: Camera Name
+        - name: after
+          in: query
+          required: true
+          schema:
+            type: number
+            title: After
+        - name: before
+          in: query
+          required: true
+          schema:
+            type: number
+            title: Before
+        - name: timelines
+          in: query
+          required: false
+          schema:
+            type: boolean
+            default: false
+            title: Timelines
+      responses:
+        '200':
+          description: Successful Response
+          content:
+            application/json:
+              schema: {}
+        '422':
+          description: Validation Error
+          content:
+            application/json:
+              schema:
+                $ref: '#/components/schemas/HTTPValidationError'
+      security:
+        - frigateUserAuth: []
+      x-required-role: camera
   /{camera_name}/recordings:
     get:
       tags:
@@ -7708,6 +7885,46 @@ components:
       required:
         - ids
       title: DeleteFaceImagesBody
+    DetectionHardware:
+      properties:
+        key:
+          type: string
+          title: Hardware key
+          description: Stable identifier for this kind of hardware.
+        detector:
+          type: string
+          title: Detector type
+          description: The detector that drives this hardware.
+        name:
+          type: string
+          title: Hardware name
+          description: Human readable name for this kind of hardware.
+        units:
+          items:
+            $ref: '#/components/schemas/HardwareUnit'
+          type: array
+          title: Units
+          description: Each physical piece of this hardware that was found.
+        count:
+          type: integer
+          title: Unit count
+          description: How many units were found.
+        unlimited:
+          type: boolean
+          title: Unlimited detectors
+          description: Whether this hardware can run more inference processes 
+            than there are units.
+      type: object
+      required:
+        - key
+        - detector
+        - name
+        - units
+        - count
+        - unlimited
+      title: DetectionHardware
+      description: A kind of detection hardware, and every unit of it that was 
+        found.
     EventCreateResponse:
       properties:
         success:
@@ -8435,6 +8652,24 @@ components:
           title: Detail
       type: object
       title: HTTPValidationError
+    HardwareUnit:
+      properties:
+        device:
+          type: string
+          title: Device string
+          description: The value to put in a model's devices list, for example 
+            'edgetpu:pci:1'.
+        label:
+          type: string
+          title: Unit label
+          description: How to identify this unit among others of the same kind, 
+            for example 'PCIe 1'.
+      type: object
+      required:
+        - device
+        - label
+      title: HardwareUnit
+      description: One physical piece of hardware.
     Last24HoursReview:
       properties:
         reviewed_alert:
@@ -8925,6 +9160,17 @@ components:
         - msg
         - type
       title: ValidationError
+    VodStreamPreference:
+      type: string
+      enum:
+        - main
+        - sub
+      title: VodStreamPreference
+      description: |-
+        Stream pin for the path-segment VOD route.
+
+        nginx-vod derives its mapping fetch URI from the playlist URL path
+        (query params are dropped), so the preference must be a path segment.
   securitySchemes:
     frigateAdminAuth:
       type: apiKey

frigate/api/app.py

@@ -71,6 +71,7 @@ from frigate.util.config import (
     find_config_file,
     redact_credential,
 )
+from frigate.util.object_names import get_categorized_object_names
 from frigate.util.schema import get_config_schema
 from frigate.util.services import (
     get_nvidia_driver_info,
@@ -291,10 +292,6 @@ def config(request: Request):
     config: dict[str, dict[str, Any]] = config_obj.model_dump(
         mode="json", warnings="none", exclude_none=True
     )
-    config["detectors"] = {
-        name: detector.model_dump(mode="json", warnings="none", exclude_none=True)
-        for name, detector in config_obj.detectors.items()
-    }
 
     # remove environment_vars for non-admin users
     if request.headers.get("remote-role") != "admin":
@@ -375,31 +372,28 @@ def config(request: Request):
         config["go2rtc"]["streams"][stream_name] = cleaned
 
     config["plus"] = {"enabled": request.app.frigate_config.plus_api.is_active()}
-    config["model"]["colormap"] = config_obj.model.colormap
-    config["model"]["all_attributes"] = config_obj.model.all_attributes
-    config["model"]["non_logo_attributes"] = config_obj.model.non_logo_attributes
 
-    # Add model plus data if plus is enabled
-    if config["plus"]["enabled"]:
-        model_path = config.get("model", {}).get("path")
-        if model_path:
-            model_json_path = FilePath(model_path).with_suffix(".json")
+    for index, model in enumerate(config_obj.models):
+        model_dict = config["models"][index]
+        model_dict["colormap"] = model.colormap
+        model_dict["all_attributes"] = model.all_attributes
+        model_dict["non_logo_attributes"] = model.non_logo_attributes
+        model_dict["labelmap"] = model.merged_labelmap
+
+        if not config["plus"]["enabled"]:
+            continue
+
+        # Add model plus data if plus is enabled
+        model_dict["plus"] = None
+
+        if model.path:
+            model_json_path = FilePath(model.path).with_suffix(".json")
+
             try:
                 with open(model_json_path) as f:
-                    model_plus_data = json.load(f)
-                config["model"]["plus"] = model_plus_data
-            except FileNotFoundError:
-                config["model"]["plus"] = None
-            except json.JSONDecodeError:
-                config["model"]["plus"] = None
-        else:
-            config["model"]["plus"] = None
-
-    # use merged labelamp
-    for detector_config in config["detectors"].values():
-        detector_config["model"]["labelmap"] = (
-            request.app.frigate_config.model.merged_labelmap
-        )
+                    model_dict["plus"] = json.load(f)
+            except (FileNotFoundError, json.JSONDecodeError):
+                pass
 
     return JSONResponse(content=config)
 
@@ -1313,9 +1307,41 @@ def get_sub_labels(
     return JSONResponse(content=sub_labels)
 
 
+@router.get(
+    "/categorized_object_names",
+    dependencies=[Depends(allow_any_authenticated())],
+    summary="Get known object names by object type",
+    description="""Returns the sub labels and attributes this install can attach,
+    grouped by object type. Unlike /sub_labels, which reflects what has already been
+    detected, this reads the config and model files, so it covers recognized face
+    names, named license plates, custom object classification categories, and the
+    detector attributes of tracked objects.""",
+)
+def categorized_object_names(
+    request: Request,
+    object_type: str | None = None,
+    allowed_cameras: list[str] = Depends(get_allowed_cameras_for_filter),
+):
+    return JSONResponse(
+        content=get_categorized_object_names(
+            request.app.frigate_config, allowed_cameras, object_type
+        )
+    )
+
+
 @router.get("/audio_labels", dependencies=[Depends(allow_any_authenticated())])
-def get_audio_labels():
+def get_audio_labels(request: Request):
     labels = load_labels("/audio-labelmap.txt", prefill=521)
+
+    # configured overrides group several audio classes under one label, and the
+    # detector merges them over the defaults at runtime. Offer them here too, or
+    # a grouped label could never be picked in the UI.
+    config: FrigateConfig = request.app.frigate_config
+    labels.update(config.audio.labelmap)
+
+    for camera in config.cameras.values():
+        labels.update(camera.audio.labelmap)
+
     return JSONResponse(content=labels)
 
 
@@ -1337,11 +1363,14 @@ def plusModels(request: Request, filterByCurrentModelDetector: bool = False):
 
     modelList = models["list"]
 
+    config: FrigateConfig = request.app.frigate_config
+    primary_model = config.primary_model
+
     # current model type
-    modelType = request.app.frigate_config.model.model_type
+    modelType = primary_model.model_type
 
     # current detectorType for comparing to supportedDetectors
-    detectorType = list(request.app.frigate_config.detectors.values())[0].type
+    detectorType = config.devices_for_model(primary_model)[0].detector
 
     validModels = []
 

frigate/api/auth.py

@@ -83,9 +83,9 @@ def require_admin_by_default():
         "/nvinfo",
         "/labels",
         "/sub_labels",
+        "/categorized_object_names",
         "/plus/models",
         "/recognized_license_plates",
-        "/classification/attributes",
         "/timeline",
         "/timeline/hourly",
         "/recordings/storage",
@@ -972,7 +972,6 @@ def delete_user(request: Request, username: str):
     summary="Update user password",
     description="Updates a user's password. Users can only change their own password unless they have admin role. Requires the current password to verify identity for non-admin users. Password must be at least 12 characters long. If user changes their own password, a new JWT cookie is automatically issued.",
 )
-@limiter.limit(limit_value=rateLimiter.get_limit)
 async def update_password(
     request: Request,
     username: str,
@@ -986,11 +985,10 @@ async def update_password(
     current_username = current_user.get("username")
     current_role = current_user.get("role")
 
-    # Only admins may target another account. This has to cover every non-admin
-    # role rather than just viewer, since custom roles are arbitrary names
-    if current_role != "admin" and current_username != username:
+    # viewers can only change their own password
+    if current_role == "viewer" and current_username != username:
         raise HTTPException(
-            status_code=403, detail="Users can only update their own password"
+            status_code=403, detail="Viewers can only update their own password"
         )
 
     HASH_ITERATIONS = request.app.frigate_config.auth.hash_iterations
@@ -1254,23 +1252,3 @@ async def get_allowed_cameras_for_filter(request: Request):
     all_camera_names = set(request.app.frigate_config.cameras.keys())
     roles_dict = request.app.frigate_config.auth.roles
     return User.get_allowed_cameras(role, roles_dict, all_camera_names)
-
-
-async def require_full_camera_access(
-    request: Request,
-    allowed_cameras: list[str] = Depends(get_allowed_cameras_for_filter),
-):
-    """Dependency for endpoints returning data that spans every camera.
-
-    Some responses cannot be meaningfully scoped to a subset of cameras, so
-    rather than filter them the endpoint is limited to callers who can already
-    see every camera. Admin and viewer always qualify; a custom role qualifies
-    only when its camera list covers all configured cameras.
-    """
-    all_camera_names = set(request.app.frigate_config.cameras.keys())
-
-    if not all_camera_names.issubset(allowed_cameras):
-        raise HTTPException(
-            status_code=403,
-            detail="Access to all cameras is required for this endpoint",
-        )

frigate/api/camera.py

@@ -33,7 +33,7 @@ from frigate.config.camera.updater import (
     CameraConfigUpdateEnum,
     CameraConfigUpdateTopic,
 )
-from frigate.config.env import substitute_frigate_vars
+from frigate.config.env import UnknownVariableError, substitute_frigate_vars
 from frigate.models import User
 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
@@ -166,7 +166,7 @@ def go2rtc_add_stream(request: Request, stream_name: str, src: str = ""):
         if src:
             try:
                 resolved_src = substitute_frigate_vars(src)
-            except KeyError:
+            except UnknownVariableError:
                 resolved_src = src
 
             if is_restricted_go2rtc_source(resolved_src):
@@ -651,6 +651,32 @@ async def _connect_onvif_camera(
     raise first_error
 
 
+def _supports_continuous_pan_tilt(nodes) -> bool:
+    """Whether any PTZ node advertises continuous pan/tilt velocity.
+
+    The web UI's directional controls issue ContinuousMove with a PanTilt
+    velocity, so continuous pan/tilt is what makes those controls usable. This
+    is intentionally narrower than ptz_supported, which is true for any device
+    exposing the ONVIF PTZ service - including zoom/focus-only varifocal lenses.
+    """
+    for node in nodes or []:
+        spaces = getattr(node, "SupportedPTZSpaces", None) or (
+            node.get("SupportedPTZSpaces") if isinstance(node, dict) else None
+        )
+        if spaces is None:
+            continue
+
+        continuous = getattr(spaces, "ContinuousPanTiltVelocitySpace", None) or (
+            spaces.get("ContinuousPanTiltVelocitySpace")
+            if isinstance(spaces, dict)
+            else None
+        )
+        if continuous:
+            return True
+
+    return False
+
+
 @router.get(
     "/onvif/probe",
     dependencies=[Depends(require_role(["admin"]))],
@@ -808,6 +834,7 @@ async def onvif_probe(
 
         # Check PTZ support and capabilities
         ptz_supported = False
+        pan_tilt_supported = False
         presets_count = 0
         autotrack_supported = False
 
@@ -841,6 +868,15 @@ async def onvif_probe(
                     logger.debug(f"Failed to get presets: {e}")
                     presets_count = 0
 
+            # Check for real (continuous) pan/tilt, which the UI controls need
+            if ptz_supported:
+                try:
+                    nodes = await ptz_service.GetNodes()
+                    pan_tilt_supported = _supports_continuous_pan_tilt(nodes)
+                    logger.debug(f"Continuous pan/tilt supported: {pan_tilt_supported}")
+                except Exception as e:
+                    logger.debug(f"Failed to read PTZ nodes for pan/tilt support: {e}")
+
             # Check for autotracking support - requires both FOV relative movement and MoveStatus
             if ptz_supported and first_profile_token and ptz_config_token:
                 # First check for FOV relative movement support
@@ -960,6 +996,7 @@ async def onvif_probe(
             "firmware_version": device_info["firmware_version"],
             "profiles_count": profiles_count,
             "ptz_supported": ptz_supported,
+            "pan_tilt_supported": pan_tilt_supported,
             "presets_count": presets_count,
             "autotrack_supported": autotrack_supported,
         }
@@ -1349,7 +1386,7 @@ def camera_set(
     | `improve_contrast` | `ON`, `OFF` |
     | `ptz_autotracker` | `ON`, `OFF` |
     | `birdseye` | `ON`, `OFF` |
-    | `birdseye_mode` | `CONTINUOUS`, `MOTION`, `OBJECTS` |
+    | `birdseye_modes` | `CONTINUOUS`, `MOTION`, `ALL_OBJECTS`, `ALERTS`, `DETECTIONS`, `NONE`, or a comma-separated combination |
     | `motion_contour_area` | integer |
     | `motion_threshold` | integer |
     | `motion_mask` | `ON`, `OFF` |

frigate/api/chat.py

@@ -50,6 +50,7 @@ from frigate.jobs.vlm_watch import (
     stop_vlm_watch_job,
 )
 from frigate.models import Event
+from frigate.util.object_names import get_categorized_object_names
 
 logger = logging.getLogger(__name__)
 
@@ -539,6 +540,11 @@ async def execute_tool(
     if tool_name == "search_objects":
         return await _execute_search_objects(request, arguments, allowed_cameras)
 
+    if tool_name == "get_categorized_object_names":
+        return JSONResponse(
+            content=_execute_get_categorized_object_names(request, allowed_cameras)
+        )
+
     if tool_name == "find_similar_objects":
         result = await _execute_find_similar_objects(
             request, arguments, allowed_cameras
@@ -591,7 +597,7 @@ async def _execute_get_live_context(
 
     try:
         frame_processor = request.app.detected_frames_processor
-        camera_state = frame_processor.camera_states.get(camera)
+        camera_state = frame_processor.get_camera_state(camera)
 
         if camera_state is None:
             return {
@@ -655,7 +661,7 @@ async def _get_live_frame_image_url(
         return None
     try:
         frame_processor = request.app.detected_frames_processor
-        if camera not in frame_processor.camera_states:
+        if frame_processor.get_camera_state(camera) is None:
             return None
         frame = frame_processor.get_current_frame(camera, {})
         if frame is None:
@@ -717,6 +723,21 @@ async def _execute_set_camera_state(
     return {"success": True, "camera": camera, "feature": feature, "value": value}
 
 
+def _execute_get_categorized_object_names(
+    request: Request,
+    allowed_cameras: list[str],
+) -> dict[str, Any]:
+    names = get_categorized_object_names(request.app.frigate_config, allowed_cameras)
+
+    if not names:
+        return {
+            "names": {},
+            "message": "No names configured; search by label or semantic_query.",
+        }
+
+    return {"names": names}
+
+
 async def _execute_tool_internal(
     tool_name: str,
     arguments: dict[str, Any],
@@ -741,6 +762,8 @@ async def _execute_tool_internal(
         except (json.JSONDecodeError, AttributeError) as e:
             logger.warning(f"Failed to extract tool result: {e}")
             return {"error": "Failed to parse tool result"}
+    elif tool_name == "get_categorized_object_names":
+        return _execute_get_categorized_object_names(request, allowed_cameras)
     elif tool_name == "find_similar_objects":
         return await _execute_find_similar_objects(request, arguments, allowed_cameras)
     elif tool_name == "set_camera_state":
@@ -773,8 +796,8 @@ async def _execute_tool_internal(
     else:
         logger.error(
             "Tool call failed: unknown tool %r. Expected one of: search_objects, find_similar_objects, "
-            "get_live_context, start_camera_watch, stop_camera_watch, get_profile_status, get_recap. "
-            "Arguments received: %s",
+            "get_categorized_object_names, get_live_context, start_camera_watch, stop_camera_watch, "
+            "get_profile_status, get_recap. Arguments received: %s",
             tool_name,
             json.dumps(arguments),
         )

frigate/api/classification.py

@@ -14,7 +14,7 @@ from fastapi.responses import JSONResponse
 from peewee import DoesNotExist
 from playhouse.shortcuts import model_to_dict
 
-from frigate.api.auth import require_full_camera_access, require_role
+from frigate.api.auth import require_role
 from frigate.api.defs.request.classification_body import (
     AudioTranscriptionBody,
     DeleteFaceImagesBody,
@@ -741,7 +741,6 @@ def get_classification_dataset(name: str):
 
 @router.get(
     "/classification/attributes",
-    dependencies=[Depends(require_full_camera_access)],
     summary="Get custom classification attributes",
     description="""Returns custom classification attributes for a given object type.
     Only includes models with classification_type set to 'attribute'.

frigate/api/defs/query/events_query_parameters.py

@@ -14,7 +14,6 @@ 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
@@ -56,7 +55,6 @@ 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/tags.py

@@ -8,6 +8,7 @@ class Tags(Enum):
     chat = "Chat"
     events = "Events"
     export = "Export"
+    hardware = "Hardware"
     classification = "Classification"
     logs = "Logs"
     media = "Media"

frigate/api/event.py

@@ -129,7 +129,6 @@ def events(
         zones = zone
 
     limit = params.limit
-    offset = params.offset
     after = params.after
     before = params.before
     time_range = params.time_range
@@ -362,15 +361,11 @@ 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, *tiebreaker)
+        .order_by(order_by)
         .limit(limit)
-        .offset(offset)
         .dicts()
         .iterator()
     )
@@ -523,7 +518,6 @@ def events_search(
     search_type = params.search_type
     include_thumbnails = params.include_thumbnails
     limit = params.limit
-    offset = params.offset
     sort = params.sort
 
     # Filters
@@ -830,9 +824,6 @@ 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():
@@ -890,7 +881,7 @@ def events_search(
         processed_events.sort(key=lambda x: x["start_time"], reverse=True)
 
     # Limit the number of events returned
-    processed_events = processed_events[offset:][:limit]
+    processed_events = processed_events[:limit]
 
     return JSONResponse(content=processed_events)
 
@@ -1322,7 +1313,7 @@ async def set_sub_label(
     if request.app.detected_frames_processor:
         tracked_obj: TrackedObject = None
 
-        for state in request.app.detected_frames_processor.camera_states.values():
+        for state in request.app.detected_frames_processor.get_camera_states():
             tracked_obj = state.tracked_objects.get(event_id)
 
             if tracked_obj is not None:
@@ -1381,7 +1372,7 @@ async def set_plate(
     if request.app.detected_frames_processor:
         tracked_obj: TrackedObject = None
 
-        for state in request.app.detected_frames_processor.camera_states.values():
+        for state in request.app.detected_frames_processor.get_camera_states():
             tracked_obj = state.tracked_objects.get(event_id)
 
             if tracked_obj is not None:

frigate/api/export.py

@@ -9,7 +9,6 @@ import zipfile
 from collections import deque
 from collections.abc import Iterator
 from pathlib import Path
-from urllib.parse import quote
 
 import psutil
 from fastapi import APIRouter, Depends, Query, Request
@@ -69,7 +68,6 @@ from frigate.jobs.export import (
 from frigate.models import Export, ExportCase, Previews, Recordings
 from frigate.record.export import (
     DEFAULT_TIME_LAPSE_FFMPEG_ARGS,
-    DEFAULT_TIME_LAPSE_FFMPEG_INPUT_ARGS,
     ChaptersEnum,
     PlaybackSourceEnum,
     validate_ffmpeg_args,
@@ -455,22 +453,6 @@ def _stream_case_archive(exports: list[Export]) -> Iterator[bytes]:
     yield from buffer.drain()
 
 
-def _content_disposition(filename: str, ascii_fallback: str) -> str:
-    """Build an attachment Content-Disposition that survives non-ASCII names.
-
-    Header values are encoded as latin-1, so a name outside that range cannot
-    go in filename at all. RFC 6266 handles this with a pair: a plain ASCII
-    filename for old clients, plus a percent-encoded UTF-8 filename* that
-    every current browser prefers.
-    """
-    ascii_name = filename if filename.isascii() else ascii_fallback
-
-    return (
-        f'attachment; filename="{ascii_name}"; '
-        f"filename*=UTF-8''{quote(filename, safe='')}"
-    )
-
-
 @router.get(
     "/cases/{case_id}/download",
     dependencies=[Depends(allow_any_authenticated())],
@@ -513,9 +495,7 @@ def download_export_case(
         _stream_case_archive(exports),
         media_type="application/zip",
         headers={
-            "Content-Disposition": _content_disposition(
-                f"{archive_base}.zip", f"{case_id}.zip"
-            ),
+            "Content-Disposition": f'attachment; filename="{archive_base}.zip"',
         },
     )
 
@@ -1013,7 +993,7 @@ def export_recording_custom(
 
     # Set default values if not provided (timelapse defaults)
     if ffmpeg_input_args is None:
-        ffmpeg_input_args = DEFAULT_TIME_LAPSE_FFMPEG_INPUT_ARGS
+        ffmpeg_input_args = ""
 
     if ffmpeg_output_args is None:
         ffmpeg_output_args = DEFAULT_TIME_LAPSE_FFMPEG_ARGS

frigate/api/fastapi_app.py

@@ -21,6 +21,7 @@ from frigate.api import (
     debug_replay,
     event,
     export,
+    hardware,
     media,
     motion_search,
     notification,
@@ -145,6 +146,7 @@ def create_fastapi_app(
     app.include_router(preview.router)
     app.include_router(notification.router)
     app.include_router(export.router)
+    app.include_router(hardware.router)
     app.include_router(event.router)
     app.include_router(media.router)
     app.include_router(motion_search.router)

frigate/api/hardware.py

@@ -0,0 +1,30 @@
+"""Hardware discovery APIs."""
+
+import logging
+
+from fastapi import APIRouter, Depends
+
+from frigate.api.auth import require_role
+from frigate.api.defs.tags import Tags
+from frigate.detectors.hardware import DetectionHardware, hardware_prober
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(tags=[Tags.hardware])
+
+
+@router.get(
+    "/hardware/probe",
+    response_model=list[DetectionHardware],
+    dependencies=[Depends(require_role(["admin"]))],
+)
+def probe_hardware(refresh: bool = False) -> list[DetectionHardware]:
+    """Get the object detection hardware attached to this system.
+
+    Args:
+        refresh: Probe again instead of returning the cached result
+
+    Returns:
+        Every kind of detection hardware that was found
+    """
+    return hardware_prober.probe(refresh=refresh)

frigate/api/media.py

@@ -6,10 +6,13 @@ import logging
 import math
 import os
 import subprocess as sp
+import tempfile
 import time
+from collections.abc import Iterator
 from datetime import UTC, datetime, timedelta
+from enum import Enum
 from pathlib import Path as FilePath
-from typing import Any
+from typing import IO, Any
 from urllib.parse import unquote
 
 import cv2
@@ -39,12 +42,14 @@ from frigate.config.camera.snapshots import SnapshotsConfig
 from frigate.const import (
     CACHE_DIR,
     INSTALL_DIR,
-    MAX_SEGMENT_DURATION,
     PREVIEW_FRAME_TYPE,
+    STREAM_TYPE_MAIN,
+    STREAM_TYPE_SUB,
 )
 from frigate.models import Event, Previews, Recordings, Regions, ReviewSegment
 from frigate.output.preview import get_most_recent_preview_frame
 from frigate.track.object_processing import TrackedObjectProcessor
+from frigate.util.ffmpeg import terminate_ffmpeg_stream
 from frigate.util.file import (
     get_event_snapshot_bytes,
     get_event_snapshot_path,
@@ -52,12 +57,40 @@ from frigate.util.file import (
     load_event_snapshot_image,
 )
 from frigate.util.image import get_image_from_recording, get_image_quality_params
-from frigate.util.media import get_keyframe_before
 from frigate.util.object import create_empty_regions_grid
+from frigate.util.recording_coverage import (
+    build_spans,
+    null_audio_glitches,
+    plan_clip,
+    resolve_coverage,
+    stream_has_audio,
+)
 
 logger = logging.getLogger(__name__)
 
 
+# must match the patched MAX_CLIPS in docker/main/build_nginx.sh; a
+# normal hour needs ~360, one clip per recording file
+NGINX_VOD_MAX_CLIPS = 1080
+
+# tail of ffmpeg's stderr kept for the clip download failure log
+CLIP_STDERR_LOG_BYTES = 8192
+
+# how long a drained clip download waits for ffmpeg to exit on its own
+CLIP_FFMPEG_EXIT_TIMEOUT = 10
+
+
+class VodStreamPreference(str, Enum):
+    """Stream pin for the path-segment VOD route.
+
+    nginx-vod derives its mapping fetch URI from the playlist URL path
+    (query params are dropped), so the preference must be a path segment.
+    """
+
+    main = STREAM_TYPE_MAIN
+    sub = STREAM_TYPE_SUB
+
+
 router = APIRouter(tags=[Tags.media])
 
 
@@ -319,7 +352,7 @@ async def get_snapshot_from_recording(
                 & (frame_time <= Recordings.end_time)
             )
             .where(Recordings.camera == camera_name)
-            .order_by(Recordings.start_time.desc())
+            .order_by(Recordings.stream_type.asc(), Recordings.start_time.desc())
             .limit(1)
             .get()
         )
@@ -338,7 +371,7 @@ async def get_snapshot_from_recording(
                     & (frame_time <= Recordings.end_time)
                 )
                 .where(Recordings.camera == camera_name)
-                .order_by(Recordings.start_time.desc())
+                .order_by(Recordings.stream_type.asc(), Recordings.start_time.desc())
                 .limit(1)
                 .get()
             )
@@ -398,7 +431,7 @@ async def submit_recording_snapshot_to_plus(
             (frame_time >= Recordings.start_time) & (frame_time <= Recordings.end_time)
         )
         .where(Recordings.camera == camera_name)
-        .order_by(Recordings.start_time.desc())
+        .order_by(Recordings.stream_type.asc(), Recordings.start_time.desc())
         .limit(1)
     )
 
@@ -441,6 +474,53 @@ async def submit_recording_snapshot_to_plus(
         )
 
 
+def _read_stderr_tail(stderr_file: IO[bytes]) -> str:
+    """Read back the last CLIP_STDERR_LOG_BYTES of a captured stderr file."""
+    stderr_file.seek(0, os.SEEK_END)
+    stderr_file.seek(max(0, stderr_file.tell() - CLIP_STDERR_LOG_BYTES))
+    return stderr_file.read().decode("utf-8", "replace")
+
+
+def _run_clip_download(ffmpeg_cmd: list[str], file_path: str) -> Iterator[bytes]:
+    """Stream an ffmpeg concat remux to the client, always cleaning up after it."""
+    stderr_file = None
+    ffmpeg = None
+
+    try:
+        stderr_file = tempfile.TemporaryFile()
+        ffmpeg = sp.Popen(ffmpeg_cmd, stdout=sp.PIPE, stderr=stderr_file)
+
+        while True:
+            data = ffmpeg.stdout.read(8192)
+
+            if not data:
+                break
+
+            yield data
+
+        try:
+            # wait rather than signal, so the real exit code survives
+            ffmpeg.wait(timeout=CLIP_FFMPEG_EXIT_TIMEOUT)
+        except sp.TimeoutExpired:
+            pass
+    finally:
+        if ffmpeg is not None:
+            # read before terminating: a None here is our teardown, not a failure
+            exit_code = ffmpeg.poll()
+            terminate_ffmpeg_stream(ffmpeg)
+
+            if exit_code:
+                logger.error(
+                    "Failed to generate clip, ffmpeg logs: %s",
+                    _read_stderr_tail(stderr_file),
+                )
+
+        if stderr_file is not None:
+            stderr_file.close()
+
+        FilePath(file_path).unlink(missing_ok=True)
+
+
 @router.get(
     "/{camera_name}/start/{start_ts}/end/{end_ts}/clip.mp4",
     dependencies=[Depends(require_camera_access)],
@@ -452,40 +532,29 @@ async def recording_clip(
     start_ts: float,
     end_ts: float,
 ):
-    def run_download(ffmpeg_cmd: list[str], file_path: str):
-        with sp.Popen(
-            ffmpeg_cmd,
-            stderr=sp.PIPE,
-            stdout=sp.PIPE,
-            text=False,
-        ) as ffmpeg:
-            while True:
-                data = ffmpeg.stdout.read(8192)
-                if data is not None and len(data) > 0:
-                    yield data
-                else:
-                    if ffmpeg.returncode and ffmpeg.returncode != 0:
-                        logger.error(
-                            f"Failed to generate clip, ffmpeg logs: {ffmpeg.stderr.read()}"
-                        )
-                    else:
-                        FilePath(file_path).unlink(missing_ok=True)
-                    break
+    def get_clip_query(stream_type: str):
+        return (
+            Recordings.select(
+                Recordings.path,
+                Recordings.start_time,
+                Recordings.end_time,
+            )
+            .where(
+                (Recordings.start_time.between(start_ts, end_ts))
+                | (Recordings.end_time.between(start_ts, end_ts))
+                | ((start_ts > Recordings.start_time) & (end_ts < Recordings.end_time))
+            )
+            .where(Recordings.camera == camera_name)
+            .where(Recordings.stream_type == stream_type)
+            .order_by(Recordings.start_time.asc())
+        )
 
-    recordings = (
-        Recordings.select(
-            Recordings.path,
-            Recordings.start_time,
-            Recordings.end_time,
-        )
-        .where(
-            (Recordings.start_time.between(start_ts, end_ts))
-            | (Recordings.end_time.between(start_ts, end_ts))
-            | ((start_ts > Recordings.start_time) & (end_ts < Recordings.end_time))
-        )
-        .where(Recordings.camera == camera_name)
-        .order_by(Recordings.start_time.asc())
-    )
+    # never mix streams in one concat; use main when available and
+    # fall back to sub for expired-main history
+    recordings = get_clip_query(STREAM_TYPE_MAIN)
+
+    if recordings.count() == 0:
+        recordings = get_clip_query(STREAM_TYPE_SUB)
 
     if recordings.count() == 0:
         return JSONResponse(
@@ -496,7 +565,9 @@ async def recording_clip(
             status_code=400,
         )
 
-    file_name = sanitize_filename(f"playlist_{camera_name}_{start_ts}-{end_ts}.txt")
+    file_name = sanitize_filename(
+        f"playlist_{camera_name}_{start_ts}-{end_ts}_{os.urandom(4).hex()}.txt"
+    )
     file_path = os.path.join(CACHE_DIR, file_name)
     with open(file_path, "w") as file:
         clip: Recordings
@@ -544,22 +615,65 @@ async def recording_clip(
     ]
 
     return StreamingResponse(
-        run_download(ffmpeg_cmd, file_path),
+        _run_clip_download(ffmpeg_cmd, file_path),
         media_type="video/mp4",
     )
 
 
-@router.get(
-    "/vod/{camera_name}/start/{start_ts}/end/{end_ts}",
-    dependencies=[Depends(require_camera_access)],
-    description="Returns an HLS playlist for the specified timestamp-range on the specified camera. Append /master.m3u8 or /index.m3u8 for HLS playback.",
-)
-async def vod_ts(
+def _build_vod_clip(
+    row: Any, start: float, end: float
+) -> tuple[dict[str, Any], int] | None:
+    """Build one nginx-vod clip dict + duration (ms) for a recording row trimmed to [start, end).
+
+    Realization comes entirely from the shared plan_clip, so the coverage
+    endpoint's realized timelines match this manifest by construction.
+    """
+    plan = plan_clip(row, start, end)
+
+    if plan.skipped:
+        return None
+
+    clip: dict[str, Any] = {"type": "source", "path": row.path}
+    if plan.clip_from_ms is not None:
+        clip["clipFrom"] = plan.clip_from_ms
+    if plan.key_frame_durations is not None:
+        # real gaps enable keyframe-aligned sub-file segments (bootstrap
+        # ladder); the whole-clip fallback keeps one segment per file,
+        # the only safe cut without an index
+        if plan.first_key_frame_offset_ms > 0:
+            clip["firstKeyFrameOffset"] = plan.first_key_frame_offset_ms
+        clip["keyFrameDurations"] = plan.key_frame_durations
+    else:
+        clip["keyFrameDurations"] = [plan.duration_ms]
+    logger.debug(
+        "VOD: added clip %s duration_ms=%s clipFrom=%s",
+        row.path,
+        plan.duration_ms,
+        clip.get("clipFrom"),
+    )
+    return clip, plan.duration_ms
+
+
+async def _vod_response(
     camera_name: str,
     start_ts: float,
     end_ts: float,
     force_discontinuity: bool = False,
-):
+    stream_preference: str | None = None,
+) -> JSONResponse:
+    """Build an nginx-vod mapping JSON for a camera over a timestamp range.
+
+    Always a single-sequence mapping; quality selection happens in the
+    frontend by choosing between this route and the stream-pinned routes.
+
+    Args:
+        camera_name: The camera to build the mapping for
+        start_ts: Range start as a unix timestamp
+        end_ts: Range end as a unix timestamp
+        force_discontinuity: Emit HLS discontinuity markers between clips
+        stream_preference: Pin the manifest to one stream type ("main" or
+            "sub"), serving only that stream's recordings
+    """
     logger.debug(
         "VOD: Generating VOD for %s from %s to %s with force_discontinuity=%s",
         camera_name,
@@ -567,104 +681,85 @@ async def vod_ts(
         end_ts,
         force_discontinuity,
     )
-    recordings = (
-        Recordings.select(
-            Recordings.path,
-            Recordings.duration,
-            Recordings.end_time,
-            Recordings.start_time,
-        )
-        .where(
-            Recordings.start_time.between(start_ts, end_ts)
-            | Recordings.end_time.between(start_ts, end_ts)
-            | ((start_ts > Recordings.start_time) & (end_ts < Recordings.end_time))
-        )
-        .where(Recordings.camera == camera_name)
-        .order_by(Recordings.start_time.asc())
-        .iterator()
+    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),
+        stream_preference,
     )
 
-    clips = []
-    durations = []
-    min_duration_ms = 100  # Minimum 100ms to ensure at least one video frame
-    max_duration_ms = MAX_SEGMENT_DURATION * 1000
-
-    recording: Recordings
-    for recording in recordings:
+    durations: list[int] = []
+    clips: list[dict[str, Any]] = []
+    # gathered after glitch-nulling and span building, so the policy
+    # decisions below reflect the manifest's real contents
+    video_codecs: set[str] = set()
+    audio_presence: set[bool] = set()
+    audio_params: set[tuple[str | None, int | None]] = set()
+    span_streams: set[bool] = set()
+    for row, span_start, span_end, span_is_main in spans:
         logger.debug(
             "VOD: processing recording: %s start=%s end=%s duration=%s",
-            recording.path,
-            recording.start_time,
-            recording.end_time,
-            recording.duration,
+            row.path,
+            row.start_time,
+            row.end_time,
+            row.duration,
         )
+        built = _build_vod_clip(row, span_start, span_end)
 
-        clip = {"type": "source", "path": recording.path}
-        duration = int(recording.duration * 1000)
-
-        # adjust start offset if start_ts is after recording.start_time
-        if start_ts > recording.start_time:
-            inpoint = int((start_ts - recording.start_time) * 1000)
-            clip["clipFrom"] = inpoint
-            duration -= inpoint
-            logger.debug(
-                "VOD: applied clipFrom %sms to %s",
-                inpoint,
-                recording.path,
-            )
-
-        # adjust end if recording.end_time is after end_ts
-        if recording.end_time > end_ts:
-            duration -= int((recording.end_time - end_ts) * 1000)
-
-        # nginx-vod-module pushes clipFrom forward to the next keyframe,
-        # which can leave too few frames and produce an empty/unplayable
-        # segment. Snap clipFrom back to the preceding keyframe so the
-        # segment always starts with a decodable frame.
-        if "clipFrom" in clip:
-            keyframe_ms = get_keyframe_before(recording.path, clip["clipFrom"])
-            if keyframe_ms is not None:
-                gained = clip["clipFrom"] - keyframe_ms
-                clip["clipFrom"] = keyframe_ms
-                duration += gained
-                logger.debug(
-                    "VOD: snapped clipFrom to keyframe at %sms for %s, duration now %sms",
-                    keyframe_ms,
-                    recording.path,
-                    duration,
-                )
-            else:
-                # could not read keyframes, remove clipFrom to use full recording
-                logger.debug(
-                    "VOD: no keyframe info for %s, removing clipFrom to use full recording",
-                    recording.path,
-                )
-                del clip["clipFrom"]
-                duration = int(recording.duration * 1000)
-                if recording.end_time > end_ts:
-                    duration -= int((recording.end_time - end_ts) * 1000)
-
-        if duration < min_duration_ms:
-            # skip if the clip has no valid duration (too short to contain frames)
-            logger.debug(
-                "VOD: skipping recording %s - resulting duration %sms too short",
-                recording.path,
-                duration,
-            )
+        if built is None:
             continue
 
-        if min_duration_ms <= duration < max_duration_ms:
-            clip["keyFrameDurations"] = [duration]
-            clips.append(clip)
-            durations.append(duration)
-            logger.debug(
-                "VOD: added clip %s duration_ms=%s clipFrom=%s",
-                recording.path,
-                duration,
-                clip.get("clipFrom"),
-            )
-        else:
-            logger.warning(f"Recording clip is missing or empty: {recording.path}")
+        clips.append(built[0])
+        durations.append(built[1])
+        span_streams.add(span_is_main)
+        if row.video_codec is not None:
+            video_codecs.add(row.video_codec)
+        audio_presence.add(row.has_audio is not False)
+        # legacy rows contribute no signature, so uniformly-unknown
+        # history keeps the legacy shape
+        if row.has_audio is not False and (
+            row.audio_codec is not None or row.audio_rate is not None
+        ):
+            audio_params.add((row.audio_codec, row.audio_rate))
+
+    # nginx-vod requires a uniform track count per sequence, and adding or
+    # removing an audio track across an MSE discontinuity is unproven
+    if len(audio_presence) > 1:
+        logger.debug(
+            "VOD: %s mixes audio-bearing and audio-less recordings between "
+            "%s and %s; serving the range without audio",
+            camera_name,
+            start_ts,
+            end_ts,
+        )
+        for clip in clips:
+            clip["tracks"] = "v"
+
+    # discontinuity mode emits per-clip init segments, letting the decoder
+    # reconfigure at each boundary. Stream type counts as a signature of
+    # its own: the two encoders differ in SPS/PPS even when codec name and
+    # audio params match, and a single-init manifest then decode-fails on
+    # players that only configure from the init segment (iOS)
+    use_discontinuity = (
+        len(video_codecs) > 1 or len(audio_params) > 1 or len(span_streams) > 1
+    )
+    if use_discontinuity:
+        logger.debug(
+            "VOD: %s mixes media signatures between %s and %s (video codecs "
+            "%s, audio params %s, streams %s); serving a discontinuity "
+            "manifest with per-clip init segments",
+            camera_name,
+            start_ts,
+            end_ts,
+            sorted(video_codecs),
+            sorted(audio_params, key=str),
+            sorted(span_streams),
+        )
 
     if not clips:
         logger.error(
@@ -678,16 +773,50 @@ async def vod_ts(
             status_code=404,
         )
 
+    if len(clips) > NGINX_VOD_MAX_CLIPS:
+        logger.warning(
+            "VOD: %s needs %d clips between %s and %s, exceeding nginx's "
+            "limit of %d; playback of this range will fail. This usually "
+            "means the camera produced abnormally short recording segments "
+            "(check the stream's timestamps)",
+            camera_name,
+            len(clips),
+            start_ts,
+            end_ts,
+            NGINX_VOD_MAX_CLIPS,
+        )
+
+    # segmentation comes from the vod_* nginx directives plus per-clip
+    # keyFrameDurations; a segment_duration field here was always ignored
+    # (nginx-vod parses only camelCase segmentDuration)
     hour_ago = datetime.now() - timedelta(hours=1)
-    return JSONResponse(
-        content={
-            "cache": hour_ago.timestamp() > start_ts,
-            "discontinuity": force_discontinuity,
-            "consistentSequenceMediaInfo": True,
-            "durations": durations,
-            "segment_duration": max(durations),
-            "sequences": [{"clips": clips}],
-        }
+    content = {
+        "cache": hour_ago.timestamp() > start_ts,
+        "discontinuity": force_discontinuity or use_discontinuity,
+        "consistentSequenceMediaInfo": True,
+        "durations": durations,
+        "sequences": [{"clips": clips}],
+    }
+    if use_discontinuity:
+        # clip-indexed naming is what makes nginx-vod emit per-clip
+        # EXT-X-MAP outside of its live mode
+        content["initialClipIndex"] = 1
+    return JSONResponse(content=content)
+
+
+@router.get(
+    "/vod/{camera_name}/start/{start_ts}/end/{end_ts}",
+    dependencies=[Depends(require_camera_access)],
+    description="Returns an HLS playlist for the specified timestamp-range on the specified camera. Append /master.m3u8 or /index.m3u8 for HLS playback.",
+)
+async def vod_ts(
+    camera_name: str,
+    start_ts: float,
+    end_ts: float,
+    force_discontinuity: bool = False,
+):
+    return await _vod_response(
+        camera_name, start_ts, end_ts, force_discontinuity=force_discontinuity
     )
 
 
@@ -776,7 +905,43 @@ async def vod_clip(
     start_ts: float,
     end_ts: float,
 ):
-    return await vod_ts(camera_name, start_ts, end_ts, force_discontinuity=True)
+    # the tracking-details player corrects its timeline from
+    # sequences[0].clips[0].clipFrom
+    return await _vod_response(
+        camera_name,
+        start_ts,
+        end_ts,
+        force_discontinuity=True,
+    )
+
+
+# registered after /vod/clip/... on purpose: both routes are six path
+# segments, Starlette matches structurally in registration order, and the
+# enum validation on {stream} would otherwise 422 every /vod/clip request
+@router.get(
+    "/vod/{camera_name}/{stream}/start/{start_ts}/end/{end_ts}",
+    dependencies=[Depends(require_camera_access)],
+    description="Returns an HLS playlist pinned to one stream type (main or sub) for the specified timestamp-range on the specified camera. Append /master.m3u8 or /index.m3u8 for HLS playback.",
+)
+async def vod_ts_stream(
+    camera_name: str,
+    stream: VodStreamPreference,
+    start_ts: float,
+    end_ts: float,
+    force_discontinuity: bool = False,
+):
+    """VOD for a timestamp range pinned to one stream type.
+
+    How the frontend selects quality, now that mappings are always
+    single-sequence.
+    """
+    return await _vod_response(
+        camera_name,
+        start_ts,
+        end_ts,
+        force_discontinuity=force_discontinuity,
+        stream_preference=stream.value,
+    )
 
 
 @router.get(
@@ -814,13 +979,13 @@ async def event_snapshot(
             timestamp_style=request.app.frigate_config.cameras[
                 event.camera
             ].timestamp_style,
-            colormap=request.app.frigate_config.model.colormap,
+            colormap=request.app.frigate_config.model_for_camera(event.camera).colormap,
         )
     except DoesNotExist:
         # see if the object is currently being tracked
         try:
             camera_states: list[CameraState] = (
-                request.app.detected_frames_processor.camera_states.values()
+                request.app.detected_frames_processor.get_camera_states()
             )
             for camera_state in camera_states:
                 if event_id in camera_state.tracked_objects:
@@ -898,7 +1063,7 @@ async def event_thumbnail(
     if thumbnail_bytes is None:
         # see if the object is currently being tracked
         try:
-            camera_states = request.app.detected_frames_processor.camera_states.values()
+            camera_states = request.app.detected_frames_processor.get_camera_states()
             for camera_state in camera_states:
                 if event_id in camera_state.tracked_objects:
                     tracked_obj = camera_state.tracked_objects.get(event_id)
@@ -1127,7 +1292,7 @@ async def event_snapshot_clean(request: Request, event_id: str, download: bool =
             # see if the object is currently being tracked
             try:
                 camera_states = (
-                    request.app.detected_frames_processor.camera_states.values()
+                    request.app.detected_frames_processor.get_camera_states()
                 )
                 for camera_state in camera_states:
                     if event_id in camera_state.tracked_objects:

frigate/api/notification.py

@@ -1,10 +1,8 @@
 """Notification apis."""
 
-import ipaddress
 import logging
 import os
 from typing import Any
-from urllib.parse import urlparse
 
 from cryptography.hazmat.primitives import serialization
 from fastapi import APIRouter, Depends, Request
@@ -21,95 +19,6 @@ logger = logging.getLogger(__name__)
 
 router = APIRouter(tags=[Tags.notifications])
 
-# Push endpoints are opaque URLs but stay well under this in practice
-MAX_ENDPOINT_LENGTH = 2048
-
-# Suffixes that only ever resolve on the local network
-INTERNAL_HOST_SUFFIXES = (".local", ".localdomain", ".internal", ".home.arpa")
-
-
-def _validate_push_endpoint(endpoint: Any) -> str | None:
-    """Return a reason the endpoint is unusable, or None when it is valid.
-
-    Subscriptions are issued by the browser vendor's push service, so a valid
-    endpoint is always a public https URL. Anything else is either a broken
-    registration or an attempt to aim the notification sender somewhere it
-    should not reach.
-    """
-    if not isinstance(endpoint, str) or not endpoint:
-        return "endpoint must be a url"
-
-    if len(endpoint) > MAX_ENDPOINT_LENGTH:
-        return "endpoint is too long"
-
-    try:
-        parsed = urlparse(endpoint)
-        port = parsed.port
-    except ValueError:
-        return "endpoint is not a valid url"
-
-    if parsed.scheme != "https":
-        return "endpoint must use https"
-
-    if parsed.username or parsed.password:
-        return "endpoint must not include credentials"
-
-    if port is not None and port != 443:
-        return "endpoint must use the default https port"
-
-    hostname = parsed.hostname
-
-    if not hostname:
-        return "endpoint must include a hostname"
-
-    try:
-        address = ipaddress.ip_address(hostname)
-    except ValueError:
-        address = None
-
-    if address is not None:
-        # A push service is never reachable at an address only this network can
-        # route, so anything non-global is a misconfiguration at best
-        if not address.is_global:
-            return "endpoint must not use a private address"
-    elif hostname == "localhost" or "." not in hostname:
-        return "endpoint must use a fully qualified hostname"
-    elif hostname.endswith(INTERNAL_HOST_SUFFIXES):
-        return "endpoint must not use an internal hostname"
-
-    # The subscription token lives in the path, and webpush.py assumes there is
-    # a separator after the host when it builds the VAPID audience
-    if len(parsed.path) <= 1:
-        return "endpoint must include a subscription path"
-
-    return None
-
-
-def _validate_subscription(sub: Any) -> str | None:
-    """Return a reason the subscription is unusable, or None when it is valid."""
-    if not isinstance(sub, dict):
-        return "subscription must be an object"
-
-    reason = _validate_push_endpoint(sub.get("endpoint"))
-
-    if reason:
-        return reason
-
-    keys = sub.get("keys")
-
-    if not isinstance(keys, dict):
-        return "subscription must include keys"
-
-    # WebPusher raises on a missing key, which would break every send for the
-    # user rather than just this registration
-    for name in ("p256dh", "auth"):
-        value = keys.get(name)
-
-        if not isinstance(value, str) or not value:
-            return f"subscription keys must include {name}"
-
-    return None
-
 
 @router.get(
     "/notifications/pubkey",
@@ -162,17 +71,6 @@ def register_notifications(request: Request, body: dict = None):
             status_code=400,
         )
 
-    reason = _validate_subscription(sub)
-
-    if reason:
-        logger.warning(
-            "Rejected notification registration for %s: %s", username, reason
-        )
-        return JSONResponse(
-            content={"success": False, "message": f"Invalid subscription: {reason}"},
-            status_code=400,
-        )
-
     try:
         User.update(notification_tokens=User.notification_tokens.append(sub)).where(
             User.username == username

frigate/api/record.py

@@ -25,8 +25,20 @@ from frigate.api.defs.query.recordings_query_parameters import (
 )
 from frigate.api.defs.response.generic_response import GenericResponse
 from frigate.api.defs.tags import Tags
-from frigate.const import RECORD_DIR
+from frigate.const import (
+    MAX_SEGMENT_DURATION,
+    RECORD_DIR,
+    STREAM_TYPE_MAIN,
+    STREAM_TYPE_SUB,
+)
 from frigate.models import Event, Recordings
+from frigate.util.recording_coverage import (
+    coverage_spans,
+    known_video_codecs,
+    realized_timelines,
+    resolve_coverage,
+    stream_media_summary,
+)
 from frigate.util.time import get_dst_transitions
 
 logger = logging.getLogger(__name__)
@@ -59,7 +71,7 @@ def get_recordings_storage_usage(request: Request):
 
 
 @router.get("/recordings/summary", dependencies=[Depends(allow_any_authenticated())])
-def all_recordings_summary(
+async def all_recordings_summary(
     request: Request,
     params: MediaRecordingsSummaryQueryParams = Depends(),
     allowed_cameras: list[str] = Depends(get_allowed_cameras_for_filter),
@@ -76,18 +88,23 @@ def all_recordings_summary(
     else:
         camera_list = allowed_cameras
 
-    time_range_query = (
-        Recordings.select(
-            fn.MIN(Recordings.start_time).alias("min_time"),
-            fn.MAX(Recordings.start_time).alias("max_time"),
+    min_time: float | None = None
+    max_time: float | None = None
+    for camera in camera_list:
+        cam_min = (
+            Recordings.select(fn.MIN(Recordings.start_time))
+            .where(Recordings.camera == camera)
+            .scalar()
         )
-        .where(Recordings.camera << camera_list)
-        .dicts()
-        .get()
-    )
-
-    min_time = time_range_query.get("min_time")
-    max_time = time_range_query.get("max_time")
+        if cam_min is None:
+            continue
+        cam_max = (
+            Recordings.select(fn.MAX(Recordings.start_time))
+            .where(Recordings.camera == camera)
+            .scalar()
+        )
+        min_time = cam_min if min_time is None else min(min_time, cam_min)
+        max_time = cam_max if max_time is None else max(max_time, cam_max)
 
     if min_time is None or max_time is None:
         return JSONResponse(content={})
@@ -97,22 +114,60 @@ def all_recordings_summary(
     days: dict[str, bool] = {}
 
     for period_start, period_end, period_offset in dst_periods:
-        day_expr = ((Recordings.start_time + period_offset) / 86400).cast("int")
+        first_start = max(min_time, period_start - MAX_SEGMENT_DURATION)
+        first_day = int((first_start + period_offset) // 86400)
+        last_day = int((min(max_time, period_end) + period_offset) // 86400)
 
-        period_query = (
-            Recordings.select(day_expr.alias("day_idx"))
-            .where(
-                (Recordings.camera << camera_list)
-                & (Recordings.end_time >= period_start)
-                & (Recordings.start_time <= period_end)
+        day_idx = first_day
+        while day_idx <= last_day:
+            day_str = (dt.date(1970, 1, 1) + dt.timedelta(days=day_idx)).isoformat()
+            day_start = day_idx * 86400 - period_offset
+            day_end = day_start + 86400
+
+            if day_str in days:
+                day_idx += 1
+                continue
+
+            if day_end <= period_end:
+                upper = Recordings.start_time < day_end
+            else:
+                upper = Recordings.start_time <= period_end
+
+            has_recordings = (
+                Recordings.select(Recordings.id)
+                .where(
+                    (Recordings.camera << camera_list)
+                    & (Recordings.end_time >= period_start)
+                    & (Recordings.start_time >= day_start)
+                    & upper
+                )
+                .exists()
             )
-            .distinct()
-            .namedtuples()
-        )
+            if has_recordings:
+                days[day_str] = True
+                day_idx += 1
+                continue
 
-        for g in period_query:
-            day_str = (dt.date(1970, 1, 1) + dt.timedelta(days=g.day_idx)).isoformat()
-            days[day_str] = True
+            # empty day
+            next_start: float | None = None
+            for camera in camera_list:
+                cam_next = (
+                    Recordings.select(fn.MIN(Recordings.start_time))
+                    .where(
+                        Recordings.camera == camera,
+                        Recordings.start_time >= day_end,
+                        Recordings.start_time <= period_end,
+                    )
+                    .scalar()
+                )
+                if cam_next is not None and (
+                    next_start is None or cam_next < next_start
+                ):
+                    next_start = cam_next
+
+            if next_start is None:
+                break
+            day_idx = max(day_idx + 1, int((next_start + period_offset) // 86400))
 
     return JSONResponse(content=dict(sorted(days.items())))
 
@@ -149,23 +204,28 @@ async def recordings_summary(camera_name: str, timezone: str = "utc"):
         period_hour_modifier = f"{hours_offset} hour"
         period_minute_modifier = f"{minutes_offset} minute"
 
+        hour_expression = fn.strftime(
+            "%Y-%m-%d %H",
+            fn.datetime(
+                Recordings.start_time,
+                "unixepoch",
+                period_hour_modifier,
+                period_minute_modifier,
+            ),
+        )
+
+        # sub rows duplicate the camera's motion/object stats, so
+        # aggregating them too would double-count
         recording_groups = (
             Recordings.select(
-                fn.strftime(
-                    "%Y-%m-%d %H",
-                    fn.datetime(
-                        Recordings.start_time,
-                        "unixepoch",
-                        period_hour_modifier,
-                        period_minute_modifier,
-                    ),
-                ).alias("hour"),
+                hour_expression.alias("hour"),
                 fn.SUM(Recordings.duration).alias("duration"),
                 fn.SUM(Recordings.motion).alias("motion"),
                 fn.SUM(Recordings.objects).alias("objects"),
             )
             .where(
                 (Recordings.camera == camera_name)
+                & (Recordings.stream_type == STREAM_TYPE_MAIN)
                 & (Recordings.end_time >= period_start)
                 & (Recordings.start_time <= period_end)
             )
@@ -174,6 +234,23 @@ async def recordings_summary(camera_name: str, timezone: str = "utc"):
             .namedtuples()
         )
 
+        # sub recordings can outlive main, so hours covered only by sub
+        # rows are reported too, flagged as sub_only
+        sub_groups = (
+            Recordings.select(
+                hour_expression.alias("hour"),
+                fn.SUM(Recordings.duration).alias("duration"),
+            )
+            .where(
+                (Recordings.camera == camera_name)
+                & (Recordings.stream_type == STREAM_TYPE_SUB)
+                & (Recordings.end_time >= period_start)
+                & (Recordings.start_time <= period_end)
+            )
+            .group_by((Recordings.start_time + period_offset).cast("int") / 3600)
+            .namedtuples()
+        )
+
         event_groups = (
             Event.select(
                 fn.strftime(
@@ -197,17 +274,43 @@ async def recordings_summary(camera_name: str, timezone: str = "utc"):
 
         event_map = {g.hour: g.count for g in event_groups}
 
-        for recording_group in recording_groups:
-            parts = recording_group.hour.split()
+        hour_stats = [
+            (
+                g.hour,
+                {
+                    "motion": g.motion,
+                    "objects": g.objects,
+                    "duration": round(g.duration),
+                },
+            )
+            for g in recording_groups
+        ]
+        main_hours = {group_hour for group_hour, _ in hour_stats}
+        hour_stats.extend(
+            (
+                g.hour,
+                {
+                    "motion": 0,
+                    "objects": 0,
+                    "duration": round(g.duration),
+                    "sub_only": True,
+                },
+            )
+            for g in sub_groups
+            if g.hour not in main_hours
+        )
+        # restore the most-recent-first ordering after merging in sub hours
+        hour_stats.sort(key=lambda entry: entry[0], reverse=True)
+
+        for group_hour, stats in hour_stats:
+            parts = group_hour.split()
             hour = parts[1]
             day = parts[0]
-            events_count = event_map.get(recording_group.hour, 0)
+            events_count = event_map.get(group_hour, 0)
             hour_data = {
                 "hour": hour,
                 "events": events_count,
-                "motion": recording_group.motion,
-                "objects": recording_group.objects,
-                "duration": round(recording_group.duration),
+                **stats,
             }
             if day in days:
                 # merge counts if already present (edge-case at DST boundary)
@@ -223,6 +326,35 @@ async def recordings_summary(camera_name: str, timezone: str = "utc"):
     return JSONResponse(content=list(days.values()))
 
 
+@router.get(
+    "/{camera_name}/recordings/coverage",
+    dependencies=[Depends(require_camera_access)],
+)
+async def recordings_coverage(
+    camera_name: str, after: float, before: float, timelines: bool = False
+):
+    """Returns merged recording coverage spans plus codec compatibility.
+
+    codecs_compatible is false only when more than one known video codec
+    appears across the range's rows, the case where the merged vod route
+    degrades to a single-stream manifest.
+    """
+    intervals = resolve_coverage(camera_name, after, before)
+
+    content = {
+        "spans": coverage_spans(intervals),
+        "codecs_compatible": len(known_video_codecs(intervals)) <= 1,
+        "streams": stream_media_summary(intervals),
+    }
+
+    # pure computation (shared plan_clip, record-time keyframe index), but
+    # opt-in for payload hygiene: day-level requests need only the spans
+    if timelines:
+        content["timelines"] = realized_timelines(intervals)
+
+    return JSONResponse(content=content)
+
+
 @router.get("/{camera_name}/recordings", dependencies=[Depends(require_camera_access)])
 async def recordings(
     camera_name: str,
@@ -243,6 +375,8 @@ async def recordings(
         )
         .where(
             Recordings.camera == camera_name,
+            Recordings.stream_type == STREAM_TYPE_MAIN,
+            Recordings.start_time >= after - MAX_SEGMENT_DURATION,
             Recordings.end_time >= after,
             Recordings.start_time <= before,
         )
@@ -282,22 +416,22 @@ async def no_recordings(
     )
     scale = params.scale
 
-    clauses = [
-        (Recordings.end_time >= after) & (Recordings.start_time <= before),
-        (Recordings.camera << camera_list),
-    ]
+    recordings: list[tuple[float, float]] = []
+    for camera in camera_list:
+        recordings.extend(
+            Recordings.select(Recordings.start_time, Recordings.end_time)
+            .where(
+                Recordings.camera == camera,
+                Recordings.start_time >= after - MAX_SEGMENT_DURATION,
+                Recordings.end_time >= after,
+                Recordings.start_time <= before,
+            )
+            .tuples()
+            .iterator()
+        )
 
-    # Get recording start times
-    data: list[Recordings] = (
-        Recordings.select(Recordings.start_time, Recordings.end_time)
-        .where(reduce(operator.and_, clauses))
-        .order_by(Recordings.start_time.asc())
-        .dicts()
-        .iterator()
-    )
-
-    # Convert recordings to list of (start, end) tuples, ordered by start_time
-    recordings = [(r["start_time"], r["end_time"]) for r in data]
+    # the merge pass below expects a single start-ordered timeline
+    recordings.sort()
 
     # Merge overlapping/adjacent recordings into covered intervals. The query
     # orders by start_time, so a single pass merges them

frigate/api/review.py

@@ -17,7 +17,6 @@ from frigate.api.auth import (
     get_allowed_cameras_for_filter,
     get_current_user,
     require_camera_access,
-    require_full_camera_access,
     require_role,
 )
 from frigate.api.defs.query.review_query_parameters import (
@@ -33,6 +32,7 @@ from frigate.api.defs.response.review_response import (
     ReviewSummaryResponse,
 )
 from frigate.api.defs.tags import Tags
+from frigate.const import STREAM_TYPE_MAIN
 from frigate.embeddings import EmbeddingsContext
 from frigate.models import Recordings, ReviewSegment, UserReviewStatus
 from frigate.review.types import SeverityEnum
@@ -43,22 +43,6 @@ 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],
@@ -108,7 +92,10 @@ async def review(
         filtered_labels = labels.split(",")
 
         for label in filtered_labels:
-            label_clauses.append(get_label_clause(label))
+            label_clauses.append(
+                (ReviewSegment.data["objects"].cast("text") % f'*"{label}"*')
+                | (ReviewSegment.data["audio"].cast("text") % f'*"{label}"*')
+            )
         clauses.append(reduce(operator.or_, label_clauses))
 
     if zones != "all":
@@ -249,7 +236,10 @@ async def review_summary(
         filtered_labels = labels.split(",")
 
         for label in filtered_labels:
-            label_clauses.append(get_label_clause(label))
+            label_clauses.append(
+                (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
@@ -347,8 +337,9 @@ async def review_summary(
         filtered_labels = labels.split(",")
 
         for label in filtered_labels:
-            label_clauses.append(get_label_clause(label, include_audio=False))
-
+            label_clauses.append(
+                ReviewSegment.data["objects"].cast("text") % f'*"{label}"*'
+            )
         clauses.append(reduce(operator.or_, label_clauses))
 
     # Find the time range of available data
@@ -607,6 +598,8 @@ def motion_activity(
 
     clauses = [(Recordings.start_time > after) & (Recordings.end_time < before)]
     clauses.append(Recordings.motion > 0)
+    # sub rows duplicate the camera's motion stats, so only count main rows
+    clauses.append(Recordings.stream_type == STREAM_TYPE_MAIN)
 
     if cameras != "all":
         requested = set(cameras.split(","))
@@ -719,7 +712,6 @@ async def get_review(request: Request, review_id: str):
     dependencies=[Depends(allow_any_authenticated())],
 )
 async def set_not_reviewed(
-    request: Request,
     review_id: str,
     current_user: dict = Depends(get_current_user),
 ):
@@ -738,8 +730,6 @@ async def set_not_reviewed(
             status_code=404,
         )
 
-    await require_camera_access(review.camera, request=request)
-
     try:
         user_review = UserReviewStatus.get(
             UserReviewStatus.user_id == user_id,
@@ -756,12 +746,9 @@ async def set_not_reviewed(
     )
 
 
-# Intentionally not camera scoped, as the summary correlates each flagged event
-# with overlapping activity on other cameras. Restricted to callers who can
-# already see every camera, so the unscoped query discloses nothing.
 @router.post(
     "/review/summarize/start/{start_ts}/end/{end_ts}",
-    dependencies=[Depends(require_full_camera_access)],
+    dependencies=[Depends(require_role(["admin"]))],
     description="Use GenAI to summarize review items over a period of time.",
 )
 def generate_review_summary(request: Request, start_ts: float, end_ts: float):

frigate/app.py

@@ -49,6 +49,8 @@ from frigate.debug_replay import (
     DebugReplayManager,
     cleanup_replay_cameras,
 )
+from frigate.detectors.detector_config import SceneEnum
+from frigate.detectors.device import build_detector_config, runner_names
 from frigate.embeddings import EmbeddingProcess, EmbeddingsContext
 from frigate.events.audio import AudioProcessor
 from frigate.events.cleanup import EventCleanup
@@ -69,6 +71,7 @@ from frigate.models import (
     User,
 )
 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.onvif import OnvifController
@@ -98,7 +101,9 @@ class FrigateApp:
         self.metrics_manager = manager
         self.audio_process: mp.Process | None = None
         self.stop_event = stop_event
-        self.detection_queue: Queue = mp.Queue()
+        self.detection_queues: dict[SceneEnum, Queue] = {
+            model.scene: mp.Queue() for model in config.models
+        }
         self.detectors: dict[str, ObjectDetectProcess] = {}
         self.detection_shms: list[mp.shared_memory.SharedMemory] = []
         self.log_queue: Queue = mp.Queue()
@@ -335,6 +340,7 @@ class FrigateApp:
             self.ptz_metrics,
             comms,
         )
+        self.dispatcher.start_communicators()
 
     def init_profile_manager(self) -> None:
         self.profile_manager = ProfileManager(
@@ -343,20 +349,19 @@ class FrigateApp:
         self.dispatcher.profile_manager = self.profile_manager
 
     def start_detectors(self) -> None:
+        model_cameras: dict[SceneEnum, list[str]] = {
+            model.scene: [] for model in self.config.models
+        }
+
         for name in self.config.cameras.keys():
+            model = self.config.model_for_camera(name)
+            model_cameras[model.scene].append(name)
+
             try:
-                largest_frame = max(
-                    [
-                        det.model.height * det.model.width * 3
-                        if det.model is not None
-                        else 320
-                        for det in self.config.detectors.values()
-                    ]
-                )
                 shm_in = UntrackedSharedMemory(
                     name=name,
                     create=True,
-                    size=largest_frame,
+                    size=detection_frame_size(model),
                 )
             except FileExistsError:
                 shm_in = UntrackedSharedMemory(name=name)
@@ -371,15 +376,26 @@ class FrigateApp:
             self.detection_shms.append(shm_in)
             self.detection_shms.append(shm_out)
 
-        for name, detector_config in self.config.detectors.items():
-            self.detectors[name] = ObjectDetectProcess(
-                name,
-                self.detection_queue,
-                list(self.config.cameras.keys()),
-                self.config,
-                detector_config,
-                self.stop_event,
-            )
+        # a device may be listed more than once to run additional inference
+        # processes on it, so names are only unique once de-duplicated
+        all_devices = [
+            device
+            for model in self.config.models
+            for device in self.config.devices_for_model(model)
+        ]
+        names = iter(runner_names(all_devices))
+
+        for model in self.config.models:
+            for device in self.config.devices_for_model(model):
+                name = next(names)
+                self.detectors[name] = ObjectDetectProcess(
+                    name,
+                    self.detection_queues[model.scene],
+                    model_cameras[model.scene],
+                    self.config,
+                    build_detector_config(device, model),
+                    self.stop_event,
+                )
 
     def start_ptz_autotracker(self) -> None:
         self.ptz_autotracker_thread = PtzAutoTrackerThread(
@@ -410,7 +426,7 @@ class FrigateApp:
     def start_camera_processor(self) -> None:
         self.camera_maintainer = CameraMaintainer(
             self.config,
-            self.detection_queue,
+            self.detection_queues,
             self.detected_frames_queue,
             self.camera_metrics,
             self.ptz_metrics,
@@ -674,8 +690,10 @@ class FrigateApp:
         for detector in self.detectors.values():
             detector.stop()
 
-        empty_and_close_queue(self.detection_queue)
-        logger.info("Detection queue closed")
+        for detection_queue in self.detection_queues.values():
+            empty_and_close_queue(detection_queue)
+
+        logger.info("Detection queues closed")
 
         self.detected_frames_processor.join()
         empty_and_close_queue(self.detected_frames_queue)

frigate/camera/activity_manager.py

@@ -18,6 +18,7 @@ from frigate.config.camera.updater import (
     CameraConfigUpdateEnum,
     CameraConfigUpdateSubscriber,
 )
+from frigate.detectors.detector_config import NON_LOGO_ATTRIBUTES
 
 logger = logging.getLogger(__name__)
 
@@ -103,13 +104,12 @@ class CameraActivityManager:
         all_objects: list[dict[str, Any]] = []
 
         for camera in new_activity.keys():
-            camera_config = self.config.cameras.get(camera)
-            if camera_config is None:
+            if camera not in self.config.cameras:
                 continue
 
             # handle cameras that were added dynamically
             if camera not in self.camera_all_object_counts:
-                self.__init_camera(camera_config)
+                self.__init_camera(self.config.cameras[camera])
 
             new_objects = new_activity[camera].get("objects", [])
             all_objects.extend(new_objects)
@@ -179,7 +179,7 @@ class CameraActivityManager:
             return
 
         for label in camera_config.objects.track:
-            if label in self.config.model.non_logo_attributes:
+            if label in NON_LOGO_ATTRIBUTES:
                 continue
 
             new_count = all_objects[label]
@@ -234,13 +234,12 @@ class AudioActivityManager:
         now = datetime.datetime.now().timestamp()
 
         for camera in new_activity.keys():
-            camera_config = self.config.cameras.get(camera)
-            if camera_config is None:
+            if camera not in self.config.cameras:
                 continue
 
             # handle cameras that were added dynamically
             if camera not in self.current_audio_detections:
-                self.__init_camera(camera_config)
+                self.__init_camera(self.config.cameras[camera])
 
             new_detections = new_activity[camera].get("detections", [])
             if self.compare_audio_activity(camera, new_detections, now):

frigate/camera/maintainer.py

@@ -15,7 +15,9 @@ 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
 from frigate.util.image import SharedMemoryFrameManager, UntrackedSharedMemory
 from frigate.util.object import get_camera_regions_grid
@@ -29,7 +31,7 @@ class CameraMaintainer(threading.Thread):
     def __init__(
         self,
         config: FrigateConfig,
-        detection_queue: Queue,
+        detection_queues: dict[SceneEnum, Queue],
         detected_frames_queue: Queue,
         camera_metrics: DictProxy,
         ptz_metrics: dict[str, PTZMetrics],
@@ -38,7 +40,7 @@ class CameraMaintainer(threading.Thread):
     ):
         super().__init__(name="camera_processor")
         self.config = config
-        self.detection_queue = detection_queue
+        self.detection_queues = detection_queues
         self.detected_frames_queue = detected_frames_queue
         self.stop_event = stop_event
         self.camera_metrics = camera_metrics
@@ -79,10 +81,11 @@ class CameraMaintainer(threading.Thread):
         # create or update region grids for each camera
         for camera in self.config.cameras.values():
             assert camera.name is not None
+            model = self.config.model_for_camera(camera.name)
             self.region_grids[camera.name] = get_camera_regions_grid(
                 camera.name,
                 camera.detect,
-                max(self.config.model.width, self.config.model.height),
+                max(model.width, model.height),
             )
 
     def __calculate_shm_frame_count(self) -> int:
@@ -114,6 +117,7 @@ class CameraMaintainer(threading.Thread):
             return
 
         camera_stop_event = self.__ensure_camera_stop_event(name)
+        model = self.config.model_for_camera(name)
 
         if runtime:
             self.camera_metrics[name] = CameraMetrics(self.metrics_manager)
@@ -123,32 +127,24 @@ class CameraMaintainer(threading.Thread):
             self.region_grids[name] = get_camera_regions_grid(
                 name,
                 config.detect,
-                max(self.config.model.width, self.config.model.height),
+                max(model.width, model.height),
             )
 
             try:
-                largest_frame = max(
-                    [
-                        det.model.height * det.model.width * 3
-                        if det.model is not None
-                        else 320
-                        for det in self.config.detectors.values()
-                    ]
-                )
                 UntrackedSharedMemory(name=f"out-{name}", create=True, size=20 * 6 * 4)
                 UntrackedSharedMemory(
                     name=name,
                     create=True,
-                    size=largest_frame,
+                    size=detection_frame_size(model),
                 )
             except FileExistsError:
                 pass
 
         camera_process = CameraTracker(
             config,
-            self.config.model,
-            self.config.model.merged_labelmap,
-            self.detection_queue,
+            model,
+            model.merged_labelmap,
+            self.detection_queues[model.scene],
             self.detected_frames_queue,
             self.camera_metrics[name],
             self.ptz_metrics[name],

frigate/camera/state.py

@@ -40,6 +40,7 @@ class CameraState:
         self.name = name
         self.config = config
         self.camera_config = config.cameras[name]
+        self.model = config.model_for_camera(name)
         self.frame_manager = frame_manager
         self.best_objects: dict[str, TrackedObject] = {}
         self.tracked_objects: dict[str, TrackedObject] = {}
@@ -60,11 +61,6 @@ class CameraState:
         # face/LPR pipelines when using a model without built-in detection.
         self.face_recognition_min_obj_area: int = 0
         self.lpr_min_obj_area: int = 0
-        self.lp_objects = {
-            label
-            for label, attributes in config.model.attributes_map.items()
-            if "license_plate" in attributes
-        }
 
         if (
             self.camera_config.face_recognition.enabled
@@ -106,9 +102,7 @@ class CameraState:
                         thickness = 1
                     else:
                         thickness = 2
-                        color = self.config.model.colormap.get(
-                            obj["label"], (255, 255, 255)
-                        )
+                        color = self.model.colormap.get(obj["label"], (255, 255, 255))
                 else:
                     thickness = 1
                     color = (255, 0, 0)
@@ -130,9 +124,7 @@ class CameraState:
                     and obj["frame_time"] == frame_time
                 ):
                     thickness = 5
-                    color = self.config.model.colormap.get(
-                        obj["label"], (255, 255, 255)
-                    )
+                    color = self.model.colormap.get(obj["label"], (255, 255, 255))
 
                     # debug autotracking zooming - show the zoom factor box
                     if (
@@ -266,9 +258,7 @@ class CameraState:
         if draw_options.get("paths"):
             for obj in tracked_objects.values():
                 if obj["frame_time"] == frame_time and obj["path_data"]:
-                    color = self.config.model.colormap.get(
-                        obj["label"], (255, 255, 255)
-                    )
+                    color = self.model.colormap.get(obj["label"], (255, 255, 255))
 
                     path_points = [
                         (
@@ -371,7 +361,7 @@ class CameraState:
         for id in new_ids:
             logger.debug(f"{self.name}: New tracked object ID: {id}")
             new_obj = tracked_objects[id] = TrackedObject(
-                self.config.model,
+                self.model,
                 self.camera_config,
                 self.config.ui,
                 self.frame_cache,
@@ -457,7 +447,7 @@ class CameraState:
                 and obj_area >= self.face_recognition_min_obj_area
                 and updated_obj.obj_data.get("sub_label") is None
             ) or (
-                obj_label in self.lp_objects
+                obj_label in ("car", "motorcycle")
                 and self.lpr_min_obj_area > 0
                 and obj_area >= self.lpr_min_obj_area
                 and updated_obj.obj_data.get("sub_label") is None
@@ -515,7 +505,7 @@ class CameraState:
                 sub_label = None
 
                 if obj.obj_data.get("sub_label"):
-                    if obj.obj_data["sub_label"][0] in self.config.model.all_attributes:
+                    if obj.obj_data["sub_label"][0] in self.model.all_attributes:
                         label = obj.obj_data["sub_label"][0]
                     else:
                         label = f"{object_type}-verified"
@@ -553,7 +543,7 @@ class CameraState:
                     current_best.thumbnail_data is not None
                     and obj.thumbnail_data is not None
                     and is_better_thumbnail(
-                        obj.thumbnail_attributes,
+                        object_type,
                         current_best.thumbnail_data,
                         obj.thumbnail_data,
                         self.camera_config.frame_shape,

frigate/comms/base_communicator.py

@@ -1,11 +1,27 @@
 from abc import ABC, abstractmethod
 from collections.abc import Callable
-from typing import Any
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+    from frigate.comms.dispatcher import Dispatcher
 
 
 class Communicator(ABC):
     """pub/sub model via specific protocol."""
 
+    def attach_dispatcher(self, dispatcher: "Dispatcher") -> None:
+        """Receive the owning dispatcher.
+
+        Transports that need more than the receiver callback (the command topic
+        surface, the snapshot API) take it here rather than reaching through the
+        bound receiver.
+        """
+        return None
+
+    def start(self) -> None:
+        """Start background I/O after receiver wiring is complete."""
+        return None
+
     @abstractmethod
     def publish(self, topic: str, payload: Any, retain: bool = False) -> None:
         """Send data via specific protocol."""

frigate/comms/dispatcher.py

@@ -11,7 +11,11 @@ from frigate.camera.activity_manager import AudioActivityManager, CameraActivity
 from frigate.comms.base_communicator import Communicator
 from frigate.comms.runtime_state import RuntimeStatePersistence
 from frigate.comms.webpush import WebPushClient
-from frigate.config import BirdseyeModeEnum, FrigateConfig
+from frigate.config import (
+    FrigateConfig,
+    birdseye_modes_from_mqtt_payload,
+    birdseye_modes_to_mqtt_payload,
+)
 from frigate.config.camera.updater import (
     CameraConfigUpdateEnum,
     CameraConfigUpdatePublisher,
@@ -45,6 +49,11 @@ from frigate.util.services import restart_frigate
 
 logger = logging.getLogger(__name__)
 
+# <camera>/<command>/<sub_command>/set, one segment longer than the rest
+SUB_COMMAND_TOPICS = frozenset({"motion_mask", "object_mask", "zone"})
+
+BARE_COMMAND_TOPICS = frozenset({"onConnect", "restart"})
+
 
 class Dispatcher:
     """Handle communication between Frigate and communicators."""
@@ -84,7 +93,7 @@ class Dispatcher:
             "recordings": self._on_recordings_command,
             "snapshots": self._on_snapshots_command,
             "birdseye": self._on_birdseye_command,
-            "birdseye_mode": self._on_birdseye_mode_command,
+            "birdseye_modes": self._on_birdseye_modes_command,
             "review_alerts": self._on_alerts_command,
             "review_detections": self._on_detections_command,
             "object_descriptions": self._on_object_description_command,
@@ -99,12 +108,114 @@ class Dispatcher:
         }
         self.profile_manager: ProfileManager | None = None
 
-        for comm in self.comms:
-            comm.subscribe(self._receive)
-
         self.web_push_client = next(
             (comm for comm in communicators if isinstance(comm, WebPushClient)), None
         )
+
+        for comm in self.comms:
+            comm.subscribe(self._receive)
+            comm.attach_dispatcher(self)
+
+    def start_communicators(self) -> None:
+        """Start communicators after dispatcher wiring is fully initialized."""
+        for comm in self.comms:
+            comm.start()
+
+    def is_command_topic(self, topic: str) -> bool:
+        """Whether a prefix-stripped topic maps to a command handler.
+
+        Transports that fan a whole topic tree in must filter on this:
+        _receive() republishes anything it does not recognize, so forwarding
+        unfiltered would echo Frigate's own publishes back.
+        """
+        parts = topic.split("/")
+
+        if topic in BARE_COMMAND_TOPICS:
+            return True
+
+        if len(parts) == 2 and parts[1] == "ptz":
+            return True
+
+        if len(parts) == 2 and parts[1] == "set":
+            return parts[0] in self._global_settings_handlers
+
+        if len(parts) == 3 and parts[2] == "set":
+            return (
+                parts[1] in self._camera_settings_handlers
+                and parts[1] not in SUB_COMMAND_TOPICS
+            )
+
+        if len(parts) == 3 and parts[2] == "suspend":
+            return parts[1] == "notifications"
+
+        if len(parts) == 4 and parts[3] == "set":
+            return parts[1] in SUB_COMMAND_TOPICS
+
+        return False
+
+    def _build_camera_activity_snapshot(self) -> tuple[dict[str, Any], dict[str, Any]]:
+        """Build the current runtime activity snapshot for reconnect consumers."""
+        camera_status = {
+            camera: status
+            for camera, status in self.camera_activity.last_camera_activity.copy().items()
+            if camera in self.config.cameras
+        }
+        audio_detections = self.audio_activity.current_audio_detections.copy()
+        cameras_with_status = camera_status.keys()
+
+        for camera in self.config.cameras.keys():
+            if camera not in cameras_with_status:
+                camera_status[camera] = {}
+
+            camera_status[camera]["config"] = {
+                "detect": self.config.cameras[camera].detect.enabled,
+                "enabled": self.config.cameras[camera].enabled,
+                "snapshots": self.config.cameras[camera].snapshots.enabled,
+                "record": self.config.cameras[camera].record.enabled,
+                "audio": self.config.cameras[camera].audio.enabled,
+                "audio_transcription": self.config.cameras[
+                    camera
+                ].audio_transcription.live_enabled,
+                "notifications": self.config.cameras[camera].notifications.enabled,
+                "notifications_suspended": int(
+                    self.web_push_client.suspended_cameras.get(camera, 0)
+                )
+                if self.web_push_client
+                and camera in self.web_push_client.suspended_cameras
+                else 0,
+                "autotracking": self.config.cameras[camera].onvif.autotracking.enabled,
+                "alerts": self.config.cameras[camera].review.alerts.enabled,
+                "detections": self.config.cameras[camera].review.detections.enabled,
+                "object_descriptions": self.config.cameras[
+                    camera
+                ].objects.genai.enabled,
+                "review_descriptions": self.config.cameras[camera].review.genai.enabled,
+            }
+
+        return camera_status, audio_detections
+
+    def publish_runtime_snapshot(
+        self,
+        publisher: Callable[[str, Any, bool], None] | None = None,
+    ) -> None:
+        """Publish the runtime snapshot for newly connected listeners."""
+        publish = publisher or self.publish
+        camera_status, audio_detections = self._build_camera_activity_snapshot()
+
+        publish("camera_activity", json.dumps(camera_status), False)
+        publish("model_state", json.dumps(self.model_state.copy()), False)
+        publish(
+            "embeddings_reindex_progress",
+            json.dumps(self.embeddings_reindex.copy()),
+            False,
+        )
+        publish("birdseye_layout", json.dumps(self.birdseye_layout.copy()), False)
+        publish("audio_detections", json.dumps(audio_detections), False)
+        publish(
+            "profile/state",
+            self.config.active_profile or "none",
+            True,
+        )
         if self.web_push_client is not None:
             self.web_push_client.set_suspension_broadcaster(self.publish)
 
@@ -123,17 +234,11 @@ class Dispatcher:
 
             try:
                 if command_type == "set":
-                    # Commands that require a sub-command (mask/zone name)
-                    sub_command_required = {
-                        "motion_mask",
-                        "object_mask",
-                        "zone",
-                    }
                     if sub_command:
                         self._camera_settings_handlers[command](
                             camera_name, sub_command, payload
                         )
-                    elif command in sub_command_required:
+                    elif command in SUB_COMMAND_TOPICS:
                         logger.error(
                             "Command %s requires a sub-command (mask/zone name)",
                             command,
@@ -156,10 +261,11 @@ class Dispatcher:
             if camera not in self.config.cameras:
                 return None
 
+            model = self.config.model_for_camera(camera)
             grid = get_camera_regions_grid(
                 camera,
                 self.config.cameras[camera].detect,
-                max(self.config.model.width, self.config.model.height),
+                max(model.width, model.height),
             )
             return grid
 
@@ -267,67 +373,11 @@ class Dispatcher:
         def handle_birdseye_layout() -> None:
             self.publish("birdseye_layout", json.dumps(self.birdseye_layout.copy()))
 
-        def handle_on_connect() -> None:
-            camera_status = {
-                camera: status
-                for camera, status in self.camera_activity.last_camera_activity.copy().items()
-                if camera in self.config.cameras
-            }
-            audio_detections = self.audio_activity.current_audio_detections.copy()
-            cameras_with_status = camera_status.keys()
-
-            for camera in self.config.cameras.keys():
-                if camera not in cameras_with_status:
-                    camera_status[camera] = {}
-
-                camera_status[camera]["config"] = {
-                    "detect": self.config.cameras[camera].detect.enabled,
-                    "enabled": self.config.cameras[camera].enabled,
-                    "snapshots": self.config.cameras[camera].snapshots.enabled,
-                    "record": self.config.cameras[camera].record.enabled,
-                    "audio": self.config.cameras[camera].audio.enabled,
-                    "audio_transcription": self.config.cameras[
-                        camera
-                    ].audio_transcription.live_enabled,
-                    "notifications": self.config.cameras[camera].notifications.enabled,
-                    "notifications_suspended": int(
-                        self.web_push_client.suspended_cameras.get(camera, 0)
-                    )
-                    if self.web_push_client
-                    and camera in self.web_push_client.suspended_cameras
-                    else 0,
-                    "autotracking": self.config.cameras[
-                        camera
-                    ].onvif.autotracking.enabled,
-                    "alerts": self.config.cameras[camera].review.alerts.enabled,
-                    "detections": self.config.cameras[camera].review.detections.enabled,
-                    "object_descriptions": self.config.cameras[
-                        camera
-                    ].objects.genai.enabled,
-                    "review_descriptions": self.config.cameras[
-                        camera
-                    ].review.genai.enabled,
-                }
-
-            self.publish("camera_activity", json.dumps(camera_status))
-            self.publish("model_state", json.dumps(self.model_state.copy()))
-            self.publish(
-                "embeddings_reindex_progress",
-                json.dumps(self.embeddings_reindex.copy()),
-            )
-            self.publish("birdseye_layout", json.dumps(self.birdseye_layout.copy()))
-            self.publish("audio_detections", json.dumps(audio_detections))
-            self.publish(
-                "profile/state",
-                self.config.active_profile or "none",
-                retain=True,
-            )
-
         def handle_notification_test() -> None:
             self.publish("notification_test", "Test notification")
 
         # Dictionary mapping topic to handlers
-        topic_handlers = {
+        topic_handlers: dict[str, Callable[[], Any]] = {
             INSERT_MANY_RECORDINGS: handle_insert_many_recordings,
             REQUEST_REGION_GRID: handle_request_region_grid,
             INSERT_PREVIEW: handle_insert_preview,
@@ -350,7 +400,7 @@ class Dispatcher:
             "jobState": handle_job_state,
             "audioTranscriptionState": handle_audio_transcription_state,
             "birdseyeLayout": handle_birdseye_layout,
-            "onConnect": handle_on_connect,
+            "onConnect": self.publish_runtime_snapshot,
         }
 
         if topic.endswith("set") or topic.endswith("ptz") or topic.endswith("suspend"):
@@ -879,11 +929,12 @@ class Dispatcher:
         )
         self.publish(f"{camera_name}/birdseye/state", payload, retain=True)
 
-    def _on_birdseye_mode_command(self, camera_name: str, payload: str) -> None:
+    def _on_birdseye_modes_command(self, camera_name: str, payload: str) -> None:
         """Callback for birdseye mode topic."""
 
-        if payload not in ["CONTINUOUS", "MOTION", "OBJECTS"]:
-            logger.info(f"Invalid birdseye_mode command: {payload}")
+        modes = birdseye_modes_from_mqtt_payload(payload)
+        if modes is None:
+            logger.info("Invalid birdseye_modes command: %s", payload)
             return
 
         birdseye_settings = self.config.cameras[camera_name].birdseye
@@ -892,16 +943,20 @@ class Dispatcher:
             logger.info(f"Birdseye mode not enabled for {camera_name}")
             return
 
-        birdseye_settings.mode = BirdseyeModeEnum(payload.lower())
+        birdseye_settings.modes = modes
         logger.info(
-            f"Setting birdseye mode for {camera_name} to {birdseye_settings.mode}"
+            f"Setting birdseye mode for {camera_name} to {birdseye_settings.modes}"
         )
 
         self.config_updater.publish_update(
             CameraConfigUpdateTopic(CameraConfigUpdateEnum.birdseye, camera_name),
             birdseye_settings,
         )
-        self.publish(f"{camera_name}/birdseye_mode/state", payload, retain=True)
+        self.publish(
+            f"{camera_name}/birdseye_modes/state",
+            birdseye_modes_to_mqtt_payload(modes),
+            retain=True,
+        )
 
     def _on_camera_notification_command(self, camera_name: str, payload: str) -> None:
         """Callback for camera level notifications topic."""

frigate/comms/inter_process.py

@@ -18,10 +18,13 @@ SOCKET_REP_REQ = "ipc:///tmp/cache/comms"
 
 class InterProcessCommunicator(Communicator):
     def __init__(self) -> None:
+        # bound eagerly so subprocesses starting before start_communicators()
+        # can still connect; their requests queue in zmq until the reader runs
         self.context = zmq.Context()
         self.socket = self.context.socket(zmq.REP)
         self.socket.bind(SOCKET_REP_REQ)
         self.stop_event: MpEvent = mp.Event()
+        self.reader_thread: threading.Thread | None = None
 
     def publish(self, topic: str, payload: Any, retain: bool = False) -> None:
         """There is no communication back to the processes."""
@@ -29,6 +32,8 @@ class InterProcessCommunicator(Communicator):
 
     def subscribe(self, receiver: Callable) -> None:
         self._dispatcher = receiver
+
+    def start(self) -> None:
         self.reader_thread = threading.Thread(target=self.read)
         self.reader_thread.start()
 
@@ -61,7 +66,8 @@ class InterProcessCommunicator(Communicator):
 
     def stop(self) -> None:
         self.stop_event.set()
-        self.reader_thread.join()
+        if self.reader_thread is not None:
+            self.reader_thread.join()
         self.socket.close(linger=0)
         self.context.destroy(linger=0)
 

frigate/comms/mqtt.py

@@ -1,16 +1,38 @@
+from __future__ import annotations
+
 import logging
+import queue
 import threading
+import time
 from collections.abc import Callable
-from typing import Any
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any
 
 import paho.mqtt.client as mqtt
 from paho.mqtt.enums import CallbackAPIVersion
 
 from frigate.comms.base_communicator import Communicator
-from frigate.config import FrigateConfig
+from frigate.config import FrigateConfig, birdseye_modes_to_mqtt_payload
+
+if TYPE_CHECKING:
+    from frigate.comms.dispatcher import Dispatcher
 
 logger = logging.getLogger(__name__)
 
+MQTT_LOOP_TIMEOUT = 1.0
+MQTT_RECONNECT_INTERVAL = 10.0
+MQTT_SHUTDOWN_FLUSH_TIMEOUT = 5.0
+MQTT_ON_CONNECT_RATE_LIMIT = 1.0
+MQTT_PUBLISH_WAIT_INTERVAL = 0.1
+
+
+@dataclass(slots=True)
+class QueuedPublish:
+    topic: str
+    payload: Any
+    retain: bool
+    done: threading.Event | None = None
+
 
 class MqttClient(Communicator):
     """Frigate wrapper for mqtt client."""
@@ -19,28 +41,80 @@ class MqttClient(Communicator):
         self.config = config
         self.mqtt_config = config.mqtt
         self.connected = False
+        self.client: mqtt.Client | None = None
+        self._dispatcher: Callable[[str, Any], Any] | None = None
+        self._command_router: Dispatcher | None = None
+        self._worker: threading.Thread | None = None
+        self._stop_event = threading.Event()
+        self._publish_queue: queue.Queue[QueuedPublish] = queue.Queue()
+        self._callback_queue: queue.Queue[tuple[Any, ...]] = queue.Queue()
+        self._retained_lock = threading.Lock()
+        self._pending_retained: dict[str, tuple[Any, bool]] = {}
+        self._inflight_retained: dict[int, tuple[str, Any]] = {}
+        self._subscription_mid: int | None = None
+        self._subscription_ready = False
+        self._next_connect_time = 0.0
+        self._last_on_connect_dispatch = 0.0
 
     def subscribe(self, receiver: Callable) -> None:
         """Wrapper for allowing dispatcher to subscribe."""
         self._dispatcher = receiver
-        self._start()
+
+    def attach_dispatcher(self, dispatcher: Dispatcher) -> None:
+        """Take Dispatcher's command surface and snapshot API."""
+        self._command_router = dispatcher
+
+    def start(self) -> None:
+        """Start the MQTT worker after all receiver wiring is complete."""
+
+        if self._worker and self._worker.is_alive():
+            return
+
+        self._stop_event.clear()
+        self._start_worker()
 
     def publish(self, topic: str, payload: Any, retain: bool = False) -> None:
         """Wrapper for publishing when client is in valid state."""
+        full_topic = f"{self.mqtt_config.topic_prefix}/{topic}"
+
         if not self.connected:
-            logger.debug(f"Unable to publish to {topic}: client is not connected")
+            if retain:
+                self._queue_retained(full_topic, payload, retain)
+            else:
+                logger.debug("Unable to publish to %s: client is not connected", topic)
             return
 
-        self.client.publish(
-            f"{self.mqtt_config.topic_prefix}/{topic}",
-            payload,
-            qos=self.config.mqtt.qos,
-            retain=retain,
-        )
+        self._publish_queue.put(QueuedPublish(full_topic, payload, retain))
 
     def stop(self) -> None:
-        self.publish("available", "stopped", retain=True)
-        self.client.disconnect()
+        if self._worker is None:
+            return
+
+        if self.connected and self._subscription_ready:
+            publish_done = threading.Event()
+            self._publish_queue.put(
+                QueuedPublish(
+                    f"{self.mqtt_config.topic_prefix}/available",
+                    "stopped",
+                    True,
+                    publish_done,
+                )
+            )
+            publish_done.wait(MQTT_SHUTDOWN_FLUSH_TIMEOUT)
+
+        self._stop_event.set()
+
+        if self.client is not None:
+            try:
+                self.client.disconnect()
+            except Exception:
+                logger.debug("MQTT disconnect raised during shutdown", exc_info=True)
+
+        if self._worker.is_alive():
+            self._worker.join(MQTT_SHUTDOWN_FLUSH_TIMEOUT + MQTT_LOOP_TIMEOUT)
+
+        self._cleanup_client()
+        self._worker = None
 
     def _notifications_enabled_in_config(self) -> bool:
         """Whether notifications are configured globally or on any camera.
@@ -54,17 +128,17 @@ class MqttClient(Communicator):
             for cam in self.config.cameras.values()
         )
 
-    def _set_initial_topics(self) -> None:
-        """Set initial state topics."""
+    def _publish_retained_state(self) -> None:
+        """Publish retained MQTT state after a successful subscribe."""
         for camera_name, camera in self.config.cameras.items():
             self.publish(
                 f"{camera_name}/enabled/state",
-                "ON" if camera.enabled_in_config else "OFF",
+                "ON" if camera.enabled else "OFF",
                 retain=True,
             )
             self.publish(
                 f"{camera_name}/recordings/state",
-                "ON" if camera.record.enabled_in_config else "OFF",
+                "ON" if camera.record.enabled else "OFF",
                 retain=True,
             )
             self.publish(
@@ -74,7 +148,7 @@ class MqttClient(Communicator):
             )
             self.publish(
                 f"{camera_name}/audio/state",
-                "ON" if camera.audio.enabled_in_config else "OFF",
+                "ON" if camera.audio.enabled else "OFF",
                 retain=True,
             )
             self.publish(
@@ -89,7 +163,7 @@ class MqttClient(Communicator):
             )
             self.publish(
                 f"{camera_name}/motion/state",
-                "ON",
+                "ON" if camera.motion.enabled else "OFF",
                 retain=True,
             )
             self.publish(
@@ -99,7 +173,7 @@ class MqttClient(Communicator):
             )
             self.publish(
                 f"{camera_name}/ptz_autotracker/state",
-                "ON" if camera.onvif.autotracking.enabled_in_config else "OFF",
+                "ON" if camera.onvif.autotracking.enabled else "OFF",
                 retain=True,
             )
             self.publish(
@@ -123,9 +197,9 @@ class MqttClient(Communicator):
                 retain=True,
             )
             self.publish(
-                f"{camera_name}/birdseye_mode/state",
+                f"{camera_name}/birdseye_modes/state",
                 (
-                    camera.birdseye.mode.value.upper()
+                    birdseye_modes_to_mqtt_payload(camera.birdseye.modes)
                     if camera.birdseye.enabled
                     else "OFF"
                 ),
@@ -133,22 +207,22 @@ class MqttClient(Communicator):
             )
             self.publish(
                 f"{camera_name}/review_alerts/state",
-                "ON" if camera.review.alerts.enabled_in_config else "OFF",
+                "ON" if camera.review.alerts.enabled else "OFF",
                 retain=True,
             )
             self.publish(
                 f"{camera_name}/review_detections/state",
-                "ON" if camera.review.detections.enabled_in_config else "OFF",
+                "ON" if camera.review.detections.enabled else "OFF",
                 retain=True,
             )
             self.publish(
                 f"{camera_name}/object_descriptions/state",
-                "ON" if camera.objects.genai.enabled_in_config else "OFF",
+                "ON" if camera.objects.genai.enabled else "OFF",
                 retain=True,
             )
             self.publish(
                 f"{camera_name}/review_descriptions/state",
-                "ON" if camera.review.genai.enabled_in_config else "OFF",
+                "ON" if camera.review.genai.enabled else "OFF",
                 retain=True,
             )
 
@@ -189,13 +263,521 @@ class MqttClient(Communicator):
         )
         self.publish("available", "online", retain=True)
 
-    def on_mqtt_command(
-        self, client: mqtt.Client, userdata: Any, message: mqtt.MQTTMessage
-    ) -> None:
-        self._dispatcher(
-            message.topic.replace(f"{self.mqtt_config.topic_prefix}/", "", 1),
-            message.payload.decode(),
+    def _create_client(self) -> mqtt.Client:
+        """Build a fresh paho client for a single connect attempt."""
+        client = mqtt.Client(
+            callback_api_version=CallbackAPIVersion.VERSION2,
+            client_id=self.mqtt_config.client_id,
+            reconnect_on_failure=False,
         )
+        client.on_connect = self._on_connect
+        client.on_disconnect = self._on_disconnect
+        client.on_message = self._on_message
+        client.on_subscribe = self._on_subscribe
+        client.on_publish = self._on_publish
+        client.will_set(
+            self.mqtt_config.topic_prefix + "/available",
+            payload="offline",
+            qos=1,
+            retain=True,
+        )
+
+        if self.mqtt_config.tls_ca_certs is not None:
+            if (
+                self.mqtt_config.tls_client_cert is not None
+                and self.mqtt_config.tls_client_key is not None
+            ):
+                client.tls_set(
+                    self.mqtt_config.tls_ca_certs,
+                    self.mqtt_config.tls_client_cert,
+                    self.mqtt_config.tls_client_key,
+                )
+            else:
+                client.tls_set(self.mqtt_config.tls_ca_certs)
+
+        if self.mqtt_config.tls_insecure is not None:
+            client.tls_insecure_set(self.mqtt_config.tls_insecure)
+
+        if self.mqtt_config.user is not None:
+            client.username_pw_set(
+                self.mqtt_config.user,
+                password=self.mqtt_config.password,
+            )
+
+        return client
+
+    def _start_worker(self) -> None:
+        self._worker = threading.Thread(
+            target=self._worker_main, name="mqtt", daemon=True
+        )
+        self._worker.start()
+        logger.info("MQTT worker started")
+
+    def _worker_main(self) -> None:
+        """Run the worker loop.
+
+        An unexpected crash disables MQTT for this session rather than taking
+        Frigate down with it, so it has to announce itself: without the offline
+        publish, consumers keep the last retained values and see a healthy
+        Frigate that has simply stopped updating.
+        """
+        try:
+            self._mqtt_loop_worker()
+        except Exception:
+            if not self._stop_event.is_set():
+                logger.exception("MQTT worker crashed, disabling MQTT for this session")
+                self._stop_event.set()
+                self._subscription_ready = False
+                self._publish_offline_availability()
+                self.connected = False
+        finally:
+            # nothing drains the queue once the loop is gone, so release any
+            # waiter here or stop() blocks for the full flush timeout
+            self._requeue_disconnected_publishes()
+            self._cleanup_client()
+
+    def _publish_offline_availability(self) -> None:
+        """Announce that MQTT is going away after a worker crash.
+
+        _cleanup_client() disconnects cleanly, which tells the broker to
+        suppress the will, so the retained topic would otherwise stay "online".
+        """
+        if self.client is None:
+            return
+
+        try:
+            message_info = self.client.publish(
+                f"{self.mqtt_config.topic_prefix}/available",
+                "offline",
+                qos=self.config.mqtt.qos,
+                retain=True,
+            )
+
+            # pumped here rather than through _wait_for_publish() so the drain
+            # that may have just crashed is not re-entered
+            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)
+                    != mqtt.MQTT_ERR_SUCCESS
+                ):
+                    break
+        except Exception:
+            logger.warning(
+                "MQTT is dormant and the broker could not be told Frigate is offline",
+                exc_info=True,
+            )
+
+    def _mqtt_loop_worker(self) -> None:
+        # The worker owns all socket I/O so reconnect, subscribe, and publish
+        # ordering stays serialized in one place.
+        while not self._stop_event.is_set():
+            if self.client is None:
+                wait_time = self._next_connect_time - time.monotonic()
+                if wait_time > 0:
+                    self._stop_event.wait(min(wait_time, MQTT_LOOP_TIMEOUT))
+                    continue
+
+                if not self._connect_client():
+                    self._next_connect_time = time.monotonic() + MQTT_RECONNECT_INTERVAL
+                    continue
+
+            assert self.client is not None
+            try:
+                result = self.client.loop(timeout=MQTT_LOOP_TIMEOUT)
+            except (OSError, mqtt.WebsocketConnectionError) as err:
+                logger.warning("MQTT loop error: %s", err)
+                self._schedule_reconnect()
+                continue
+
+            self._drain_callback_queue()
+            self._drain_publish_queue()
+
+            if self._stop_event.is_set():
+                break
+
+            if result != mqtt.MQTT_ERR_SUCCESS and self.client is not None:
+                logger.error("MQTT loop returned error code: %s", result)
+                self._schedule_reconnect()
+
+    def _connect_client(self) -> bool:
+        """Create and connect a new client instance owned by the worker thread."""
+        try:
+            self.client = self._create_client()
+            self.client.connect(self.mqtt_config.host, self.mqtt_config.port, 60)
+        except Exception as err:
+            logger.error("Unable to connect to MQTT server: %s", err)
+            self._cleanup_client()
+            return False
+
+        return True
+
+    def _cleanup_client(self) -> None:
+        """Drop session-specific state and release the current paho client."""
+        self.connected = False
+        self._subscription_ready = False
+        self._subscription_mid = None
+        self._requeue_inflight_retained()
+
+        client = self.client
+        self.client = None
+
+        if client is None:
+            return
+
+        try:
+            client.disconnect()
+        except Exception:
+            logger.debug("MQTT client cleanup raised disconnect error", exc_info=True)
+
+    def _schedule_reconnect(self) -> None:
+        """Tear down the current session and arm the next reconnect attempt."""
+        if self._stop_event.is_set():
+            return
+
+        self.connected = False
+        self._subscription_ready = False
+        self._subscription_mid = None
+        self._requeue_disconnected_publishes()
+        self._next_connect_time = time.monotonic() + MQTT_RECONNECT_INTERVAL
+        logger.info("MQTT reconnect scheduled in %.1fs", MQTT_RECONNECT_INTERVAL)
+        self._cleanup_client()
+
+    def _requeue_inflight_retained(self) -> None:
+        """Rebuffer retained publishes paho took but the broker never acked.
+
+        Dropping the client drops paho's outbound queue with it, and the session
+        is clean, so the broker will not resume delivery on the new one.
+        """
+        with self._retained_lock:
+            # mids are insertion ordered, so collapsing by topic keeps the
+            # newest value when several updates to one topic were in flight
+            latest = {
+                topic: payload for topic, payload in self._inflight_retained.values()
+            }
+            self._inflight_retained.clear()
+
+        for topic, payload in latest.items():
+            self._queue_retained(topic, payload, True, overwrite=False)
+
+    def _buffer_undelivered(
+        self, queued_publish: QueuedPublish, overwrite: bool = True
+    ) -> None:
+        """Handle a publish that never reached the broker.
+
+        Releasing the waiter matters on every path: stop() blocks on it, so a
+        broker error would otherwise stall shutdown for the full flush timeout.
+        """
+        if queued_publish.retain:
+            self._queue_retained(
+                queued_publish.topic,
+                queued_publish.payload,
+                queued_publish.retain,
+                overwrite=overwrite,
+            )
+
+        if queued_publish.done is not None:
+            queued_publish.done.set()
+
+    def _requeue_disconnected_publishes(self) -> None:
+        while True:
+            try:
+                queued_publish = self._publish_queue.get_nowait()
+            except queue.Empty:
+                break
+
+            self._buffer_undelivered(queued_publish)
+
+    def _drain_callback_queue(self) -> None:
+        # Paho callbacks only enqueue transport events; state transitions run
+        # here on the worker thread.
+        while True:
+            try:
+                event = self._callback_queue.get_nowait()
+            except queue.Empty:
+                break
+
+            event_type = event[0]
+
+            if event_type == "connect":
+                self._handle_connect_event(event[1])
+            elif event_type == "connect_failure":
+                self._handle_connect_failure(event[1])
+            elif event_type == "disconnect":
+                self._handle_disconnect_event(event[1])
+            elif event_type == "subscribed":
+                self._handle_subscribe_event(event[1], event[2])
+            elif event_type == "message":
+                self._handle_inbound_message(event[1], event[2])
+            elif event_type == "published":
+                self._handle_publish_event(event[1])
+
+    def _drain_publish_queue(self) -> None:
+        """Publish queued work only after the session is fully subscribed.
+
+        Oldest first: the outage buffer replays before the queue, so a topic
+        that changed since the reconnect ends up on its newest value rather
+        than being reverted by the replay.
+        """
+        if self.connected and not self._subscription_ready:
+            return
+
+        self._flush_pending_retained()
+
+        while True:
+            try:
+                queued_publish = self._publish_queue.get_nowait()
+            except queue.Empty:
+                break
+
+            if not self.connected:
+                self._buffer_undelivered(queued_publish)
+                continue
+
+            self._publish_direct(queued_publish)
+
+    def _flush_pending_retained(self) -> None:
+        """Replay the latest retained state once the broker session is ready."""
+        if not self.connected or not self._subscription_ready:
+            return
+
+        with self._retained_lock:
+            pending = list(self._pending_retained.items())
+            self._pending_retained.clear()
+
+        for topic, (payload, retain) in pending:
+            self._publish_direct(QueuedPublish(topic, payload, retain))
+
+    def _publish_direct(self, queued_publish: QueuedPublish) -> None:
+        """Publish a queued message from the worker thread's serialized context.
+
+        The waiter is released however this exits. The message is already off
+        the queue by now, so nothing else can recover it for a stop() that is
+        blocked waiting on it.
+        """
+        try:
+            if self.client is None:
+                # never attempted, so anything already buffered for this topic
+                # was written later and has to survive
+                self._buffer_undelivered(queued_publish, overwrite=False)
+                return
+
+            try:
+                message_info = self.client.publish(
+                    queued_publish.topic,
+                    queued_publish.payload,
+                    qos=self.config.mqtt.qos,
+                    retain=queued_publish.retain,
+                )
+            except (OSError, mqtt.WebsocketConnectionError) as err:
+                logger.warning(
+                    "MQTT publish failed for %s: %s", queued_publish.topic, err
+                )
+                # a newer buffered value for this topic wins over the failed one
+                self._buffer_undelivered(queued_publish, overwrite=False)
+                self._schedule_reconnect()
+                return
+
+            if message_info.rc != mqtt.MQTT_ERR_SUCCESS:
+                logger.error(
+                    "Unable to publish to %s: mqtt error %s",
+                    queued_publish.topic,
+                    message_info.rc,
+                )
+                self._buffer_undelivered(queued_publish, overwrite=False)
+                self._schedule_reconnect()
+                return
+
+            # a successful rc only means paho accepted the message; above qos 0
+            # it is not durable until the broker acks, so keep a copy for replay
+            if queued_publish.retain and not message_info.is_published():
+                with self._retained_lock:
+                    self._inflight_retained[message_info.mid] = (
+                        queued_publish.topic,
+                        queued_publish.payload,
+                    )
+
+            if queued_publish.done is not None:
+                self._wait_for_publish(message_info)
+        finally:
+            if queued_publish.done is not None:
+                queued_publish.done.set()
+
+    def _handle_publish_event(self, mid: int) -> None:
+        """Drop the replay copy once the broker has acknowledged the message."""
+        with self._retained_lock:
+            self._inflight_retained.pop(mid, None)
+
+    def _wait_for_publish(self, message_info: mqtt.MQTTMessageInfo) -> None:
+        """Pump the loop until a shutdown-critical publish is acknowledged."""
+        deadline = time.monotonic() + MQTT_SHUTDOWN_FLUSH_TIMEOUT
+
+        while not message_info.is_published() and time.monotonic() < deadline:
+            if self.client is None:
+                return
+
+            try:
+                result = self.client.loop(timeout=MQTT_PUBLISH_WAIT_INTERVAL)
+            except (OSError, mqtt.WebsocketConnectionError) as err:
+                logger.warning("MQTT publish wait failed: %s", err)
+                self._schedule_reconnect()
+                return
+
+            self._drain_callback_queue()
+
+            if result != mqtt.MQTT_ERR_SUCCESS:
+                logger.error(
+                    "MQTT loop returned error code while waiting for publish: %s",
+                    result,
+                )
+                self._schedule_reconnect()
+                return
+
+    def _queue_retained(
+        self,
+        topic: str,
+        payload: Any,
+        retain: bool,
+        overwrite: bool = True,
+    ) -> None:
+        """Store the last retained value per topic for replay after reconnect."""
+        with self._retained_lock:
+            if overwrite or topic not in self._pending_retained:
+                self._pending_retained[topic] = (payload, retain)
+
+    def _handle_connect_event(self, reason_code: mqtt.ReasonCode) -> None:  # type: ignore[name-defined]
+        """Begin a new session by subscribing before any replay is published."""
+        if self.client is None:
+            return
+
+        self.connected = True
+        self._subscription_ready = False
+        self._subscription_mid = None
+        logger.debug("MQTT connected")
+
+        try:
+            result, mid = self.client.subscribe(
+                f"{self.mqtt_config.topic_prefix}/#",
+                qos=self.config.mqtt.qos,
+            )
+        except (OSError, mqtt.WebsocketConnectionError) as err:
+            logger.warning("MQTT subscribe failed: %s", err)
+            self._schedule_reconnect()
+            return
+
+        if result != mqtt.MQTT_ERR_SUCCESS:
+            logger.error(
+                "Unable to subscribe to MQTT command tree: mqtt error %s", result
+            )
+            self._schedule_reconnect()
+            return
+
+        self._subscription_mid = mid
+
+    def _handle_connect_failure(self, reason_code: mqtt.ReasonCode) -> None:  # type: ignore[name-defined]
+        """Record a failed connect attempt and transition into reconnect state."""
+        self.connected = False
+        logger.error(
+            "Unable to connect to MQTT server: %s", self._reason_code_name(reason_code)
+        )
+        self._schedule_reconnect()
+
+    def _handle_disconnect_event(self, reason_code: mqtt.ReasonCode) -> None:  # type: ignore[name-defined]
+        """Handle broker disconnects idempotently from the worker thread."""
+        if not self.connected:
+            return
+
+        self.connected = False
+        self._subscription_ready = False
+        self._subscription_mid = None
+
+        if self._stop_event.is_set():
+            logger.debug("MQTT disconnected")
+            self._cleanup_client()
+            return
+
+        logger.error("MQTT disconnected: %s", self._reason_code_name(reason_code))
+        self._schedule_reconnect()
+
+    def _handle_subscribe_event(
+        self,
+        mid: int,
+        reason_codes: list[mqtt.ReasonCode],  # type: ignore[name-defined]
+    ) -> None:
+        """Mark the session ready after SUBACK, then replay retained/runtime state."""
+        if mid != self._subscription_mid:
+            return
+
+        if any(
+            getattr(reason_code, "is_failure", False) for reason_code in reason_codes
+        ):
+            logger.error("MQTT subscription was rejected by the broker")
+            self._schedule_reconnect()
+            return
+
+        self._subscription_ready = True
+        self._subscription_mid = None
+
+        # a bug in replay should cost a snapshot, not the MQTT session
+        try:
+            self._publish_retained_state()
+
+            if self._command_router is not None:
+                self._command_router.publish_runtime_snapshot(self.publish)
+        except Exception:
+            logger.exception("Error replaying MQTT state after subscribe")
+
+    def _handle_inbound_message(self, topic: str, payload: str) -> None:
+        """Forward supported command topics into Dispatcher semantics."""
+        if self._dispatcher is None:
+            return
+
+        if not self._is_supported_command_topic(topic):
+            return
+
+        if topic == "onConnect":
+            now = time.monotonic()
+            if now - self._last_on_connect_dispatch < MQTT_ON_CONNECT_RATE_LIMIT:
+                logger.debug("Skipping MQTT onConnect replay request due to rate limit")
+                return
+            self._last_on_connect_dispatch = now
+
+        # a raise here used to end the network thread and take MQTT down
+        try:
+            self._dispatcher(topic, payload)
+        except Exception:
+            logger.exception("Error handling MQTT command topic %s", topic)
+
+    def _is_supported_command_topic(self, topic: str) -> bool:
+        """Filter the wildcard subscription down to Dispatcher's command surface.
+
+        Load-bearing rather than an optimization: the broker echoes Frigate's own
+        publishes back through frigate/#, and Dispatcher republishes topics it
+        does not recognize, so forwarding unfiltered would loop.
+        """
+        if self._command_router is None:
+            return False
+
+        # mirrors the gate on the state topic in _publish_retained_state()
+        if topic == "notifications/set" and not self._notifications_enabled_in_config():
+            return False
+
+        return self._command_router.is_command_topic(topic)
+
+    def _strip_topic_prefix(self, topic: str) -> str:
+        return topic.replace(f"{self.mqtt_config.topic_prefix}/", "", 1)
+
+    def _is_success_reason_code(self, reason_code: mqtt.ReasonCode) -> bool:  # type: ignore[name-defined]
+        if hasattr(reason_code, "is_failure"):
+            return not bool(reason_code.is_failure)
+
+        return bool(reason_code == 0)
+
+    def _reason_code_name(self, reason_code: mqtt.ReasonCode) -> str:  # type: ignore[name-defined]
+        if hasattr(reason_code, "getName"):
+            return str(reason_code.getName())
+
+        return str(reason_code)
 
     def _on_connect(
         self,
@@ -205,29 +787,11 @@ class MqttClient(Communicator):
         reason_code: mqtt.ReasonCode,  # type: ignore[name-defined]
         properties: Any,
     ) -> None:
-        """Mqtt connection callback."""
-        threading.current_thread().name = "mqtt"
-        if reason_code != 0:
-            if reason_code == "Server unavailable":
-                logger.error(
-                    "Unable to connect to MQTT server: MQTT Server unavailable"
-                )
-            elif reason_code == "Bad user name or password":
-                logger.error(
-                    "Unable to connect to MQTT server: MQTT Bad username or password"
-                )
-            elif reason_code == "Not authorized":
-                logger.error("Unable to connect to MQTT server: MQTT Not authorized")
-            else:
-                logger.error(
-                    "Unable to connect to MQTT server: Connection refused. Error code: %s",
-                    reason_code.getName(),
-                )
-
-        self.connected = True
-        logger.debug("MQTT connected")
-        client.subscribe(f"{self.mqtt_config.topic_prefix}/#", qos=self.config.mqtt.qos)
-        self._set_initial_topics()
+        """Handle broker connect notifications from paho."""
+        if self._is_success_reason_code(reason_code):
+            self._callback_queue.put(("connect", reason_code))
+        else:
+            self._callback_queue.put(("connect_failure", reason_code))
 
     def _on_disconnect(
         self,
@@ -237,126 +801,63 @@ class MqttClient(Communicator):
         reason_code: mqtt.ReasonCode,  # type: ignore[name-defined]
         properties: Any,
     ) -> None:
-        """Mqtt disconnection callback."""
-        self.connected = False
-        logger.error("MQTT disconnected")
+        """Handle broker disconnect notifications from paho."""
+        self._callback_queue.put(("disconnect", reason_code))
 
-    def _start(self) -> None:
-        """Start mqtt client."""
-        self.client = mqtt.Client(
-            callback_api_version=CallbackAPIVersion.VERSION2,
-            client_id=self.mqtt_config.client_id,
-        )
-        self.client.on_connect = self._on_connect
-        self.client.on_disconnect = self._on_disconnect
-        self.client.will_set(
-            self.mqtt_config.topic_prefix + "/available",
-            payload="offline",
-            qos=1,
-            retain=True,
-        )
+    def _on_subscribe(
+        self,
+        client: mqtt.Client,
+        userdata: Any,
+        mid: int,
+        reason_codes: list[mqtt.ReasonCode],  # type: ignore[name-defined]
+        properties: Any,
+    ) -> None:
+        """Handle subscribe acknowledgements from paho."""
+        self._callback_queue.put(("subscribed", mid, reason_codes))
 
-        # register callbacks
-        callback_types = [
-            "enabled",
-            "recordings",
-            "snapshots",
-            "detect",
-            "audio",
-            "audio_transcription",
-            "motion",
-            "improve_contrast",
-            "ptz_autotracker",
-            "motion_threshold",
-            "motion_contour_area",
-            "birdseye",
-            "birdseye_mode",
-            "review_alerts",
-            "review_detections",
-            "object_descriptions",
-            "review_descriptions",
-            "notifications",
-        ]
+    def _on_publish(
+        self,
+        client: mqtt.Client,
+        userdata: Any,
+        mid: int,
+        reason_code: mqtt.ReasonCode,  # type: ignore[name-defined]
+        properties: Any,
+    ) -> None:
+        """Handle publish acknowledgements from paho.
 
-        for name in self.config.cameras.keys():
-            for callback in callback_types:
-                self.client.message_callback_add(
-                    f"{self.mqtt_config.topic_prefix}/{name}/{callback}/set",
-                    self.on_mqtt_command,
-                )
+        Only tracked retained messages need an event. At the default qos 0
+        nothing is tracked, so this stays off the hot publish path.
+        """
+        with self._retained_lock:
+            if mid not in self._inflight_retained:
+                return
 
-            # notifications suspend doesn't follow the /set topic pattern
-            self.client.message_callback_add(
-                f"{self.mqtt_config.topic_prefix}/{name}/notifications/suspend",
-                self.on_mqtt_command,
-            )
+        self._callback_queue.put(("published", mid))
 
-            if self.config.cameras[name].onvif.host:
-                self.client.message_callback_add(
-                    f"{self.mqtt_config.topic_prefix}/{name}/ptz",
-                    self.on_mqtt_command,
-                )
+    def _on_message(
+        self,
+        client: mqtt.Client,
+        userdata: Any,
+        message: mqtt.MQTTMessage,
+    ) -> None:
+        """Queue inbound MQTT messages for processing in the worker loop."""
+        topic = self._strip_topic_prefix(message.topic)
 
-            for mask_name in self.config.cameras[name].motion.mask.keys():
-                self.client.message_callback_add(
-                    f"{self.mqtt_config.topic_prefix}/{name}/motion_mask/{mask_name}/set",
-                    self.on_mqtt_command,
-                )
-
-            for mask_name in self.config.cameras[name].objects.mask.keys():
-                self.client.message_callback_add(
-                    f"{self.mqtt_config.topic_prefix}/{name}/object_mask/{mask_name}/set",
-                    self.on_mqtt_command,
-                )
-
-            for zone_name in self.config.cameras[name].zones.keys():
-                self.client.message_callback_add(
-                    f"{self.mqtt_config.topic_prefix}/{name}/zone/{zone_name}/set",
-                    self.on_mqtt_command,
-                )
-
-        if self._notifications_enabled_in_config():
-            self.client.message_callback_add(
-                f"{self.mqtt_config.topic_prefix}/notifications/set",
-                self.on_mqtt_command,
-            )
-
-        self.client.message_callback_add(
-            f"{self.mqtt_config.topic_prefix}/profile/set",
-            self.on_mqtt_command,
-        )
-
-        self.client.message_callback_add(
-            f"{self.mqtt_config.topic_prefix}/onConnect", self.on_mqtt_command
-        )
-
-        self.client.message_callback_add(
-            f"{self.mqtt_config.topic_prefix}/restart", self.on_mqtt_command
-        )
-
-        if self.mqtt_config.tls_ca_certs is not None:
-            if (
-                self.mqtt_config.tls_client_cert is not None
-                and self.mqtt_config.tls_client_key is not None
-            ):
-                self.client.tls_set(
-                    self.mqtt_config.tls_ca_certs,
-                    self.mqtt_config.tls_client_cert,
-                    self.mqtt_config.tls_client_key,
-                )
-            else:
-                self.client.tls_set(self.mqtt_config.tls_ca_certs)
-        if self.mqtt_config.tls_insecure is not None:
-            self.client.tls_insecure_set(self.mqtt_config.tls_insecure)
-        if self.mqtt_config.user is not None:
-            self.client.username_pw_set(
-                self.mqtt_config.user, password=self.mqtt_config.password
-            )
-        try:
-            # https://stackoverflow.com/a/55390477
-            # with connect_async, retries are handled automatically
-            self.client.connect_async(self.mqtt_config.host, self.mqtt_config.port, 60)
-            self.client.loop_start()
-        except Exception as e:
-            logger.error(f"Unable to connect to MQTT server: {e}")
+        # Ignore everything outside Frigate's command surface before decoding or
+        # dispatching into the rest of the app.
+        if not self._is_supported_command_topic(topic):
             return
+
+        try:
+            payload = message.payload.decode()
+        except UnicodeDecodeError:
+            logger.debug("Ignoring non-UTF-8 MQTT payload for topic %s", topic)
+            return
+
+        self._callback_queue.put(
+            (
+                "message",
+                topic,
+                payload,
+            )
+        )

frigate/comms/webpush.py

@@ -63,14 +63,8 @@ class WebPushClient(Communicator):
         self.last_notification_time: float = 0
         self.user_cameras: dict[str, set[str]] = {}
         self.notification_queue: queue.Queue[PushNotification] = queue.Queue()
-        self.notification_thread = threading.Thread(
-            target=self._process_notifications, daemon=True
-        )
-        self.notification_thread.start()
-        self.suspension_thread = threading.Thread(
-            target=self._process_suspensions, daemon=True
-        )
-        self.suspension_thread.start()
+        self.notification_thread: threading.Thread | None = None
+        self.suspension_thread: threading.Thread | None = None
 
         if not self.config.notifications.email:
             logger.warning("Email must be provided for push notifications to be sent.")
@@ -99,6 +93,16 @@ class WebPushClient(Communicator):
         """Wrapper for allowing dispatcher to subscribe."""
         pass
 
+    def start(self) -> None:
+        self.notification_thread = threading.Thread(
+            target=self._process_notifications, daemon=True
+        )
+        self.notification_thread.start()
+        self.suspension_thread = threading.Thread(
+            target=self._process_suspensions, daemon=True
+        )
+        self.suspension_thread.start()
+
     def check_registrations(self) -> None:
         # check for valid claim or create new one
         now = datetime.datetime.now().timestamp()
@@ -220,7 +224,9 @@ class WebPushClient(Communicator):
         if topic == "reviews":
             decoded = json.loads(payload)
             camera = decoded["before"]["camera"]
-            if not self.config.cameras[camera].notifications.enabled:
+            camera_config = self.config.cameras.get(camera)
+
+            if camera_config is None or not camera_config.notifications.enabled:
                 return
             if self.is_camera_suspended(camera):
                 logger.debug(f"Notifications for {camera} are currently suspended.")
@@ -234,13 +240,14 @@ class WebPushClient(Communicator):
 
             # ensure notifications are enabled and the specific trigger has
             # notification action enabled
+            camera_config = self.config.cameras.get(camera)
+
             if (
-                not self.config.cameras[camera].notifications.enabled
-                or name not in self.config.cameras[camera].semantic_search.triggers
+                camera_config is None
+                or not camera_config.notifications.enabled
+                or name not in camera_config.semantic_search.triggers
                 or "notification"
-                not in self.config.cameras[camera]
-                .semantic_search.triggers[name]
-                .actions
+                not in camera_config.semantic_search.triggers[name].actions
             ):
                 return
 
@@ -251,7 +258,9 @@ class WebPushClient(Communicator):
         elif topic == "camera_monitoring":
             decoded = json.loads(payload)
             camera = decoded["camera"]
-            if not self.config.cameras[camera].notifications.enabled:
+            camera_config = self.config.cameras.get(camera)
+
+            if camera_config is None or not camera_config.notifications.enabled:
                 return
             if self.is_camera_suspended(camera):
                 logger.debug(f"Notifications for {camera} are currently suspended.")
@@ -421,7 +430,6 @@ class WebPushClient(Communicator):
         # Don't notify if message is an update and important fields don't have an update
         if (
             state == "update"
-            and payload["before"]["severity"] == payload["after"]["severity"]
             and len(payload["before"]["data"]["objects"])
             == len(payload["after"]["data"]["objects"])
             and len(payload["before"]["data"]["zones"])
@@ -603,4 +611,5 @@ class WebPushClient(Communicator):
 
     def stop(self) -> None:
         logger.info("Closing notification queue")
-        self.notification_thread.join()
+        if self.notification_thread is not None:
+            self.notification_thread.join()

frigate/comms/ws.py

@@ -466,7 +466,6 @@ class WebSocketClient(Communicator):
 
     def subscribe(self, receiver: Callable) -> None:
         self._dispatcher = receiver
-        self.start()
 
     def start(self) -> None:
         """Start the websocket client."""

frigate/config/camera/audio.py

@@ -41,6 +41,11 @@ class AudioConfig(FrigateBaseModel):
         title="Listen types",
         description="List of audio event types to detect (for example: bark, fire_alarm, speech, yell).",
     )
+    labelmap: dict[int, str] = Field(
+        default_factory=dict,
+        title="Audio labelmap customization",
+        description="Overrides or remapping entries to merge into the standard audio labelmap.",
+    )
     filters: dict[str, AudioFilterConfig] | None = Field(
         None,
         title="Audio filters",

frigate/config/camera/birdseye.py

@@ -9,21 +9,57 @@ __all__ = [
     "BirdseyeConfig",
     "BirdseyeLayoutConfig",
     "BirdseyeModeEnum",
+    "birdseye_modes_from_mqtt_payload",
+    "birdseye_modes_to_mqtt_payload",
 ]
 
+# canonical MQTT payload for an empty mode list
+MQTT_NO_MODES = "NONE"
+
 
 class BirdseyeModeEnum(str, Enum):
-    objects = "objects"
-    motion = "motion"
     continuous = "continuous"
+    motion = "motion"
+    all_objects = "all_objects"
+    alerts = "alerts"
+    detections = "detections"
 
-    @classmethod
-    def get_index(cls, type):
-        return list(cls).index(type)
 
-    @classmethod
-    def get(cls, index):
-        return list(cls)[index]
+def birdseye_modes_from_mqtt_payload(payload: str) -> list[BirdseyeModeEnum] | None:
+    """Parse an uppercase MQTT payload into activity modes, or None when invalid."""
+    raw_modes = payload.split(",")
+
+    if any(not raw_mode or raw_mode != raw_mode.upper() for raw_mode in raw_modes):
+        return None
+
+    if raw_modes == [MQTT_NO_MODES]:
+        return []
+
+    modes: list[BirdseyeModeEnum] = []
+
+    for raw_mode in raw_modes:
+        try:
+            mode = BirdseyeModeEnum(raw_mode.lower())
+        except ValueError:
+            return None
+
+        if mode in modes:
+            return None
+
+        modes.append(mode)
+
+    return modes
+
+
+def birdseye_modes_to_mqtt_payload(modes: list[BirdseyeModeEnum]) -> str:
+    """Serialize activity modes for MQTT state topics."""
+    payload = ",".join(mode.value.upper() for mode in BirdseyeModeEnum if mode in modes)
+    return payload or MQTT_NO_MODES
+
+
+def default_birdseye_modes() -> list[BirdseyeModeEnum]:
+    """Return the default Birdseye activity modes."""
+    return [BirdseyeModeEnum.all_objects]
 
 
 class BirdseyeLayoutConfig(FrigateBaseModel):
@@ -47,10 +83,10 @@ class BirdseyeConfig(FrigateBaseModel):
         title="Enable Birdseye",
         description="Enable or disable the Birdseye view feature.",
     )
-    mode: BirdseyeModeEnum = Field(
-        default=BirdseyeModeEnum.objects,
-        title="Tracking mode",
-        description="Mode for including cameras in Birdseye: 'objects', 'motion', or 'continuous'.",
+    modes: list[BirdseyeModeEnum] = Field(
+        default_factory=default_birdseye_modes,
+        title="Activity types",
+        description="Activity types that include cameras in Birdseye.",
     )
 
     restream: bool = Field(
@@ -102,10 +138,10 @@ class BirdseyeCameraConfig(BaseModel):
         title="Enable Birdseye",
         description="Enable or disable the Birdseye view feature.",
     )
-    mode: BirdseyeModeEnum = Field(
-        default=BirdseyeModeEnum.objects,
-        title="Tracking mode",
-        description="Mode for including cameras in Birdseye: 'objects', 'motion', or 'continuous'.",
+    modes: list[BirdseyeModeEnum] = Field(
+        default_factory=default_birdseye_modes,
+        title="Activity types",
+        description="Activity types that include cameras in Birdseye.",
     )
 
     order: int = Field(

frigate/config/camera/camera.py

@@ -3,7 +3,12 @@ from enum import Enum
 
 from pydantic import Field, PrivateAttr, model_validator
 
-from frigate.const import CACHE_DIR, CACHE_SEGMENT_FORMAT, REGEX_CAMERA_NAME
+from frigate.const import (
+    CACHE_DIR,
+    CACHE_SEGMENT_FORMAT,
+    REGEX_CAMERA_NAME,
+    SUB_CACHE_TAG,
+)
 from frigate.ffmpeg_presets import (
     parse_preset_hardware_acceleration_decode,
     parse_preset_hardware_acceleration_scale,
@@ -294,6 +299,28 @@ class CameraConfig(FrigateBaseModel):
                 + ffmpeg_output_args
             )
 
+        if (
+            "record_sub" in ffmpeg_input.roles
+            and self.record.enabled
+            and self.record.sub.enabled
+        ):
+            sub_output_args = self.ffmpeg.output_args.effective_record_sub
+            record_args = get_ffmpeg_arg_list(
+                parse_preset_output_record(
+                    sub_output_args,
+                    self.ffmpeg.apple_compatibility,
+                )
+                or sub_output_args
+            )
+
+            ffmpeg_output_args = (
+                record_args
+                + [
+                    f"{os.path.join(CACHE_DIR, self.name)}{SUB_CACHE_TAG}@{CACHE_SEGMENT_FORMAT}.mp4"
+                ]
+                + ffmpeg_output_args
+            )
+
         # if there aren't any outputs enabled for this input
         if len(ffmpeg_output_args) == 0:
             return None

frigate/config/camera/detect.py

@@ -1,5 +1,7 @@
 from pydantic import Field, model_validator
 
+from frigate.detectors.detector_config import SceneEnum
+
 from ..base import FrigateBaseModel
 
 __all__ = ["DetectConfig", "StationaryConfig", "StationaryMaxFramesConfig"]
@@ -60,6 +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(
+        default=SceneEnum.all,
+        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'.",
+    )
     fps: int = Field(
         default=5,
         title="Detect FPS",

frigate/config/camera/ffmpeg.py

@@ -42,6 +42,20 @@ class FfmpegOutputArgsConfig(FrigateBaseModel):
         title="Record output arguments",
         description="Default output arguments for record role streams.",
     )
+    record_sub: str | list[str] = Field(
+        default_factory=list,
+        title="Sub stream record output arguments",
+        description="Output arguments for record_sub role streams. The record output arguments are used when this is not set.",
+    )
+
+    @property
+    def effective_record_sub(self) -> str | list[str]:
+        """Output arguments used for the record_sub role.
+
+        Falls back to the record arguments rather than to the stock preset so
+        that a customized record value keeps applying to both recorded streams.
+        """
+        return self.record_sub or self.record
 
 
 class FfmpegConfig(FrigateBaseModel):
@@ -99,6 +113,7 @@ class FfmpegConfig(FrigateBaseModel):
 class CameraRoleEnum(str, Enum):
     audio = "audio"
     record = "record"
+    record_sub = "record_sub"
     detect = "detect"
 
 

frigate/config/camera/record.py

@@ -13,6 +13,7 @@ __all__ = [
     "RecordExportConfig",
     "RecordPreviewConfig",
     "RecordQualityEnum",
+    "RecordSubConfig",
     "EventsConfig",
     "ReviewRetainConfig",
     "RecordRetainConfig",
@@ -110,6 +111,34 @@ class RecordExportConfig(FrigateBaseModel):
     )
 
 
+class RecordSubConfig(FrigateBaseModel):
+    enabled: bool = Field(
+        default=False,
+        title="Enable sub stream recording",
+        description="Enable recording of a second, lower quality stream for adaptive quality playback and extended retention.",
+    )
+    continuous: RecordRetainConfig = Field(
+        default_factory=RecordRetainConfig,
+        title="Sub stream continuous retention",
+        description="Number of days to retain sub stream recordings regardless of tracked objects or motion.",
+    )
+    motion: RecordRetainConfig = Field(
+        default_factory=RecordRetainConfig,
+        title="Sub stream motion retention",
+        description="Number of days to retain sub stream recordings triggered by motion.",
+    )
+    alerts: ReviewRetainConfig = Field(
+        default_factory=ReviewRetainConfig,
+        title="Sub stream alert retention",
+        description="Retention settings for sub stream recordings of alerts.",
+    )
+    detections: ReviewRetainConfig = Field(
+        default_factory=ReviewRetainConfig,
+        title="Sub stream detection retention",
+        description="Retention settings for sub stream recordings of detections.",
+    )
+
+
 class RecordConfig(FrigateBaseModel):
     enabled: bool = Field(
         default=False,
@@ -151,12 +180,35 @@ class RecordConfig(FrigateBaseModel):
         title="Preview config",
         description="Settings controlling the quality of recording previews shown in the UI.",
     )
+    sub: RecordSubConfig = Field(
+        default_factory=RecordSubConfig,
+        title="Sub stream recording",
+        description="Settings for recording a second, lower quality stream.",
+    )
     enabled_in_config: bool | None = Field(
         default=None,
         title="Original recording state",
         description="Indicates whether recording was enabled in the original static configuration.",
     )
 
+    @property
+    def effective_alert_days(self) -> float:
+        """Alert retention extended to the sub stream window when sub is enabled.
+
+        Review items and tracked objects must stay visible for as long as
+        either stream still has recordings.
+        """
+        if self.sub.enabled:
+            return max(self.alerts.retain.days, self.sub.alerts.days)
+        return self.alerts.retain.days
+
+    @property
+    def effective_detection_days(self) -> float:
+        """Detection retention extended to the sub window when sub is enabled."""
+        if self.sub.enabled:
+            return max(self.detections.retain.days, self.sub.detections.days)
+        return self.detections.retain.days
+
     @property
     def event_pre_capture(self) -> int:
         return max(

frigate/config/camera/updater.py

@@ -96,6 +96,7 @@ class CameraConfigUpdateSubscriber:
             return
         elif update_type == CameraConfigUpdateEnum.remove:
             self.config.cameras.pop(camera, None)
+            self.config.drop_camera_model(camera)
             self.camera_configs.pop(camera, None)
             return
 
@@ -129,8 +130,13 @@ class CameraConfigUpdateSubscriber:
             config.objects = updated_config
         elif update_type == CameraConfigUpdateEnum.record:
             old_enabled_in_config = config.record.enabled_in_config
+            old_sub_enabled = config.record.sub.enabled
             config.record = updated_config
-            if old_enabled_in_config != updated_config.enabled_in_config:
+            # the record and record_sub ffmpeg outputs are gated on these
+            if (
+                old_enabled_in_config != updated_config.enabled_in_config
+                or old_sub_enabled != updated_config.sub.enabled
+            ):
                 config.recreate_ffmpeg_cmds()
         elif update_type == CameraConfigUpdateEnum.review:
             config.review = updated_config

frigate/config/config.py

@@ -11,7 +11,6 @@ from pydantic import (
     BaseModel,
     ConfigDict,
     Field,
-    TypeAdapter,
     ValidationInfo,
     field_validator,
     model_validator,
@@ -19,8 +18,9 @@ from pydantic import (
 from ruamel.yaml import YAML
 
 from frigate.const import REGEX_JSON
-from frigate.detectors import DetectorConfig, ModelConfig
-from frigate.detectors.detector_config import BaseDetectorConfig
+from frigate.detectors import ModelConfig
+from frigate.detectors.detector_config import SceneEnum
+from frigate.detectors.device import DeviceParseError, DeviceSpec, parse_device
 from frigate.plus import PlusApi
 from frigate.util.builtin import (
     deep_merge,
@@ -63,7 +63,7 @@ from .classification import (
     SemanticSearchModelEnum,
 )
 from .database import DatabaseConfig
-from .env import EnvVars
+from .env import EnvVars, reload_sources
 from .logger import LoggerConfig
 from .mqtt import MqttConfig
 from .network import NetworkingConfig
@@ -79,9 +79,14 @@ logger = logging.getLogger(__name__)
 
 yaml = YAML()
 
-# Pydantic field default applied when an existing config omits `detectors:`.
+# Pydantic field default applied when an existing config omits `models:`.
 # Kept as cpu tflite for backwards compatibility with 0.17 configs.
-DEFAULT_DETECTORS = {"cpu": {"type": "cpu"}}
+DEFAULT_MODELS = [{"devices": ["cpu"]}]
+
+
+def _default_models() -> list[ModelConfig]:
+    return [ModelConfig.model_validate(model) for model in DEFAULT_MODELS]
+
 
 # Used by the openvino branch below and rendered into the new-config YAML
 # template so first-time setups default to openvino on CPU.
@@ -93,7 +98,7 @@ DEFAULT_MODEL = {
     "path": "/openvino-model/ssdlite_mobilenet_v2.xml",
     "labelmap_path": "/openvino-model/coco_91cl_bkgr.txt",
 }
-NEW_CONFIG_DETECTORS = {"ov": {"type": "openvino", "device": "CPU"}}
+NEW_CONFIG_MODELS = [{"devices": ["openvino:CPU"], **DEFAULT_MODEL}]
 DEFAULT_DETECT_DIMENSIONS = {"width": 1280, "height": 720}
 
 
@@ -109,7 +114,7 @@ DEFAULT_CONFIG = f"""
 mqtt:
   enabled: False
 
-{_render_default_yaml({"detectors": NEW_CONFIG_DETECTORS, "model": DEFAULT_MODEL})}
+{_render_default_yaml({"models": NEW_CONFIG_MODELS})}
 cameras: {{}}  # No cameras defined, UI wizard should be used
 version: {CURRENT_CONFIG_VERSION}
 """
@@ -255,6 +260,15 @@ def verify_config_roles(camera_config: CameraConfig) -> None:
             f"Camera {camera_config.name} has record enabled, but record is not assigned to an input."
         )
 
+    if (
+        camera_config.record.enabled
+        and camera_config.record.sub.enabled
+        and "record_sub" not in assigned_roles
+    ):
+        raise ValueError(
+            f"Camera {camera_config.name} has sub stream recording enabled, but record_sub is not assigned to an input."
+        )
+
     if camera_config.audio.enabled and "audio" not in assigned_roles:
         raise ValueError(
             f"Camera {camera_config.name} has audio events enabled, but audio is not assigned to an input."
@@ -275,13 +289,11 @@ def verify_valid_live_stream_names(
             )
 
 
-def verify_recording_segments_setup_with_reasonable_time(
-    camera_config: CameraConfig,
+def verify_record_output_args_segment_time(
+    camera_config: CameraConfig, output_args: str | list[str], role: str
 ) -> None:
-    """Verify that recording segments are setup and segment time is not greater than 60."""
-    record_args: list[str] = get_ffmpeg_arg_list(
-        camera_config.ffmpeg.output_args.record
-    )
+    """Verify that a recording role's output args segment at a reasonable time."""
+    record_args: list[str] = get_ffmpeg_arg_list(output_args)
 
     if record_args[0].startswith("preset"):
         return
@@ -291,16 +303,32 @@ def verify_recording_segments_setup_with_reasonable_time(
     except ValueError:
         raise ValueError(
             f"Camera {camera_config.name} has no segment_time in \
-                         recording output args, segment args are required for record."
+                         {role} output args, segment args are required for record."
         ) from None
 
     if int(record_args[seg_arg_index + 1]) > 60:
         raise ValueError(
-            f"Camera {camera_config.name} has invalid segment_time output arg, \
+            f"Camera {camera_config.name} has invalid segment_time in {role} output args, \
                          segment_time must be 60 or less."
         )
 
 
+def verify_recording_segments_setup_with_reasonable_time(
+    camera_config: CameraConfig,
+) -> None:
+    """Verify that recording segments are setup and segment time is not greater than 60."""
+    verify_record_output_args_segment_time(
+        camera_config, camera_config.ffmpeg.output_args.record, "recording"
+    )
+
+    if camera_config.record.sub.enabled:
+        verify_record_output_args_segment_time(
+            camera_config,
+            camera_config.ffmpeg.output_args.effective_record_sub,
+            "sub stream recording",
+        )
+
+
 def verify_zone_objects_are_tracked(camera_config: CameraConfig) -> None:
     """Verify that user has not entered zone objects that are not in the tracking config."""
     for zone_name, zone in camera_config.zones.items():
@@ -497,16 +525,11 @@ class FrigateConfig(FrigateBaseModel):
         description="User interface preferences such as timezone, time/date formatting, and units.",
     )
 
-    # Detector config
-    detectors: dict[str, BaseDetectorConfig] = Field(
-        default=DEFAULT_DETECTORS,
-        title="Detector hardware",
-        description="Configuration for object detectors (CPU, GPU, ONNX backends) and any detector-specific model settings.",
-    )
-    model: ModelConfig = Field(
-        default_factory=ModelConfig,
-        title="Detection model",
-        description="Settings to configure a custom object detection model and its input shape.",
+    # Detection model config
+    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.",
     )
 
     # GenAI config (named provider configs: name -> GenAIConfig)
@@ -621,11 +644,226 @@ class FrigateConfig(FrigateBaseModel):
     )
 
     _plus_api: PlusApi
+    _model_devices: dict[SceneEnum, list[DeviceSpec]]
+    _camera_models: dict[str, ModelConfig]
+    _all_attributes: list[str]
+    _all_attribute_logos: list[str]
+    _all_attributes_map: dict[str, list[str]]
+    _all_labels: set[str]
 
     @property
     def plus_api(self) -> PlusApi:
         return self._plus_api
 
+    @property
+    def all_attributes(self) -> list[str]:
+        """Every attribute label across all configured models."""
+        return self._all_attributes
+
+    @property
+    def all_attribute_logos(self) -> list[str]:
+        """Every logo attribute label across all configured models."""
+        return self._all_attribute_logos
+
+    @property
+    def all_attributes_map(self) -> dict[str, list[str]]:
+        """Object label to attribute labels, merged across all configured models."""
+        return self._all_attributes_map
+
+    @property
+    def all_labels(self) -> set[str]:
+        """Every object label across all configured models."""
+        return self._all_labels
+
+    @property
+    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:
+                return model
+
+        return self.models[0]
+
+    def model_for_camera(self, camera_name: str) -> ModelConfig:
+        """Get the detection model a camera runs on.
+
+        Cameras added at runtime (wizard, clone, debug replay) are inserted
+        into cameras after parse, so they miss the cache built during
+        post_validation and are resolved here on first lookup.
+
+        Args:
+            camera_name: Name of the camera
+
+        Returns:
+            The model matching the camera's detect scene
+        """
+        model = self._camera_models.get(camera_name)
+
+        if model is None:
+            camera = self.cameras.get(camera_name)
+            scene = camera.detect.scene if camera is not None else SceneEnum.all
+            model = self._resolve_camera_model(camera_name, scene)
+            self._camera_models[camera_name] = model
+
+        return model
+
+    def drop_camera_model(self, camera_name: str) -> None:
+        """Forget the cached model for a camera removed at runtime.
+
+        A later re-add resolves fresh, so a camera recreated under the same
+        name with a different detect scene doesn't inherit the removed
+        camera's model.
+
+        Args:
+            camera_name: Name of the removed camera
+        """
+        self._camera_models.pop(camera_name, None)
+
+    def devices_for_model(self, model: ModelConfig) -> list[DeviceSpec]:
+        """Get the parsed hardware devices a model runs on.
+
+        Args:
+            model: One of the configured models
+
+        Returns:
+            The parsed device specs, in config order
+        """
+        return self._model_devices[model.scene]
+
+    def _load_model(self, model: ModelConfig, detector: str) -> ModelConfig:
+        """Apply detector specific defaults to a model and load its weights and labels.
+
+        Args:
+            model: The configured model
+            detector: The detector type the model runs on
+
+        Returns:
+            The loaded model
+        """
+        model_config = model.model_dump(exclude_unset=True, warnings="none")
+
+        if "path" not in model_config:
+            if detector == "cpu" or detector.endswith("_tfl"):
+                model_config["path"] = "/cpu_model.tflite"
+            elif detector == "edgetpu":
+                model_config["path"] = "/edgetpu_model.tflite"
+            elif detector == "openvino":
+                for default_key, default_value in DEFAULT_MODEL.items():
+                    model_config.setdefault(default_key, default_value)
+
+        loaded = ModelConfig.model_validate(model_config)
+        loaded.check_and_load_plus_model(self.plus_api, detector)
+        loaded.compute_model_hash()
+        return loaded
+
+    def _load_models(self) -> None:
+        """Validate the configured models and load each one."""
+        if not self.models:
+            raise ValueError("At least one model must be configured under models")
+
+        model_devices: dict[SceneEnum, list[DeviceSpec]] = {}
+        # device string -> the scene of the model that already claimed it
+        claimed_devices: dict[str, SceneEnum] = {}
+
+        for index, model in enumerate(self.models):
+            scene = model.scene.value
+
+            if model.scene in model_devices:
+                raise ValueError(
+                    f"Multiple models are configured with a scene of '{scene}'. Each model must use a different scene."
+                )
+
+            if not model.devices:
+                raise ValueError(
+                    f"Model '{scene}' must list at least one entry under devices."
+                )
+
+            try:
+                devices = [parse_device(device) for device in model.devices]
+            except DeviceParseError as err:
+                raise ValueError(
+                    f"Model '{scene}' has an invalid device: {err}"
+                ) from err
+
+            detectors = {device.detector for device in devices}
+
+            if len(detectors) > 1:
+                raise ValueError(
+                    f"Model '{scene}' mixes the {', '.join(sorted(detectors))} detectors. All of a model's devices must use the same detector."
+                )
+
+            for device in devices:
+                if device.raw in claimed_devices and not device.shareable:
+                    other = claimed_devices[device.raw]
+                    where = (
+                        f"twice by model '{scene}'"
+                        if other == model.scene
+                        else f"by both the '{other.value}' 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
+
+            self.models[index] = self._load_model(model, devices[0].detector)
+            model_devices[model.scene] = devices
+
+        attributes: set[str] = set()
+        attribute_logos: set[str] = set()
+        attributes_map: dict[str, set[str]] = {}
+        labels: set[str] = set()
+
+        for model in self.models:
+            attributes.update(model.all_attributes)
+            attribute_logos.update(model.all_attribute_logos)
+            labels.update(model.merged_labelmap.values())
+
+            for label, label_attributes in model.attributes_map.items():
+                attributes_map.setdefault(label, set()).update(label_attributes)
+
+        self._model_devices = model_devices
+        self._all_attributes = sorted(attributes)
+        self._all_attribute_logos = sorted(attribute_logos)
+        self._all_attributes_map = {
+            label: sorted(label_attributes)
+            for label, label_attributes in sorted(attributes_map.items())
+        }
+        self._all_labels = labels
+
+    def _resolve_camera_model(self, name: str, scene: SceneEnum) -> 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.
+
+        Args:
+            name: Name of the camera
+            scene: The camera's detect scene, which defaults to 'all'
+
+        Returns:
+            The model the camera runs on
+        """
+        by_scene = {model.scene: model for model in self.models}
+        model = by_scene.get(scene)
+
+        if model is not None:
+            return model
+
+        default = by_scene.get(SceneEnum.all)
+
+        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'."
+            )
+
+        logger.warning(
+            "Camera '%s' has a detect scene of '%s', but no model is configured for that scene, so the 'all' model is used",
+            name,
+            scene.value,
+        )
+        return default
+
     @model_validator(mode="after")
     def post_validation(self, info: ValidationInfo) -> Self:
         # Load plus api from context, if possible.
@@ -670,8 +908,10 @@ class FrigateConfig(FrigateBaseModel):
                     "'embeddings' in its roles for semantic search."
                 )
 
+        self._load_models()
+
         # set default min_score for object attributes
-        for attribute in self.model.all_attributes:
+        for attribute in self.all_attributes:
             existing = self.objects.filters.get(attribute)
             if existing is None:
                 self.objects.filters[attribute] = FilterConfig(min_score=0.7)
@@ -721,44 +961,7 @@ class FrigateConfig(FrigateBaseModel):
             exclude_unset=True,
         )
 
-        for key, detector in self.detectors.items():
-            adapter = TypeAdapter(DetectorConfig)
-            model_dict = (
-                detector
-                if isinstance(detector, dict)
-                else detector.model_dump(warnings="none")
-            )
-            detector_config: BaseDetectorConfig = adapter.validate_python(model_dict)
-
-            # users should not set model themselves
-            if detector_config.model:
-                logger.warning(
-                    "The model key should be specified at the root level of the config, not under detectors. The nested model key will be ignored."
-                )
-                detector_config.model = None
-
-            model_config = self.model.model_dump(exclude_unset=True, warnings="none")
-
-            if detector_config.model_path:
-                model_config["path"] = detector_config.model_path
-
-            if "path" not in model_config:
-                if detector_config.type == "cpu" or detector_config.type.endswith(
-                    "_tfl"
-                ):
-                    model_config["path"] = "/cpu_model.tflite"
-                elif detector_config.type == "edgetpu":
-                    model_config["path"] = "/edgetpu_model.tflite"
-                elif detector_config.type == "openvino":
-                    for default_key, default_value in DEFAULT_MODEL.items():
-                        model_config.setdefault(default_key, default_value)
-
-            model = ModelConfig.model_validate(model_config)
-            model.check_and_load_plus_model(self.plus_api, detector_config.type)
-            model.compute_model_hash()
-            labelmap_objects = model.merged_labelmap.values()
-            detector_config.model = model
-            self.detectors[key] = detector_config
+        self._camera_models = {}
 
         for name, camera in self.cameras.items():
             modified_global_config = global_config.copy()
@@ -785,6 +988,9 @@ class FrigateConfig(FrigateBaseModel):
                 {"name": name, **merged_config}
             )
 
+            camera_model = self._resolve_camera_model(name, camera_config.detect.scene)
+            self._camera_models[name] = camera_model
+
             if camera_config.ffmpeg.hwaccel_args == "auto":
                 camera_config.ffmpeg.hwaccel_args = self.ffmpeg.hwaccel_args
 
@@ -1005,7 +1211,7 @@ class FrigateConfig(FrigateBaseModel):
             verify_profile_overrides_match_base(camera_config)
             verify_autotrack_zones(camera_config)
             verify_motion_and_detect(camera_config)
-            verify_objects_track(camera_config, labelmap_objects)
+            verify_objects_track(camera_config, camera_model.merged_labelmap.values())
             verify_lpr_and_face(self, camera_config)
 
         # Validate camera profiles reference top-level profile definitions
@@ -1022,8 +1228,16 @@ class FrigateConfig(FrigateBaseModel):
             config.name = name
 
         self.objects.parse_all_objects(self.cameras)
-        self.model.create_colormap(sorted(self.objects.all_objects))
-        self.model.check_and_load_plus_model(self.plus_api)
+
+        # every model shares one colormap so a label is drawn the same color no
+        # matter which model detected it, so filter attributes across all models
+        # rather than letting each model filter with only its own
+        colored_labels = sorted(
+            set(self.objects.all_objects) - set(self.all_attributes)
+        )
+
+        for model in self.models:
+            model.create_colormap(colored_labels)
 
         # Check audio transcription and audio detection requirements
         if self.audio_transcription.enabled:
@@ -1093,6 +1307,9 @@ class FrigateConfig(FrigateBaseModel):
 
     @classmethod
     def parse(cls, config, *, is_json=None, safe_load=False, **context):
+        # Pick up secrets.yaml edits without a restart.
+        reload_sources()
+
         # If config is a file, read its contents.
         if hasattr(config, "read"):
             fname = getattr(config, "name", None)

frigate/config/env.py

@@ -1,20 +1,193 @@
+"""Environment variable and secrets handling for the Frigate config."""
+
+import logging
 import os
 import re
+from collections.abc import Mapping
 from pathlib import Path
-from typing import Annotated
+from typing import Annotated, Any
 
 from pydantic import AfterValidator, ValidationInfo
+from ruamel.yaml import YAML, YAMLError
 
-FRIGATE_ENV_VARS = {k: v for k, v in os.environ.items() if k.startswith("FRIGATE_")}
-secrets_dir = os.environ.get("CREDENTIALS_DIRECTORY", "/run/secrets")
-# read secret files as env vars too
-if os.path.isdir(secrets_dir) and os.access(secrets_dir, os.R_OK):
-    for secret_file in os.listdir(secrets_dir):
-        if secret_file.startswith("FRIGATE_"):
-            FRIGATE_ENV_VARS[secret_file] = (
-                Path(os.path.join(secrets_dir, secret_file)).read_text().strip()
+from frigate.const import CONFIG_DIR
+
+logger = logging.getLogger(__name__)
+
+
+class UnknownVariableError(ValueError):
+    """Undefined {FRIGATE_*} placeholder. ValueError so pydantic names the field."""
+
+
+# Substitution sources, lowest precedence first.
+_CONFIG_ENV_VARS: dict[str, str] = {}
+_SECRETS_FILE: dict[str, str] = {}
+# Snapshot: apply_config_env_vars() writes os.environ after import.
+_CONTAINER_ENV: dict[str, str] = {
+    k: v for k, v in os.environ.items() if k.startswith("FRIGATE_")
+}
+_CREDENTIALS_DIR: dict[str, str] = {}
+
+_SOURCES: tuple[tuple[str, dict[str, str]], ...] = (
+    ("environment_vars config block", _CONFIG_ENV_VARS),
+    ("secrets.yaml", _SECRETS_FILE),
+    ("container environment", _CONTAINER_ENV),
+    ("credentials directory", _CREDENTIALS_DIR),
+)
+
+FRIGATE_ENV_VARS: dict[str, str] = {}
+
+_WARNED_COLLISIONS: set[str] = set()
+
+
+def _rebuild(warn: bool = True) -> None:
+    """Merge the sources into FRIGATE_ENV_VARS.
+
+    warn=False is for the import-time call, before logging is configured.
+    """
+    merged: dict[str, str] = {}
+    origin: dict[str, str] = {}
+    duplicated: set[str] = set()
+
+    for label, source in _SOURCES:
+        for key, value in source.items():
+            if key in merged and merged[key] != value:
+                duplicated.add(key)
+
+            merged[key] = value
+            origin[key] = label
+
+    if warn:
+        for key in sorted(duplicated - _WARNED_COLLISIONS):
+            _WARNED_COLLISIONS.add(key)
+            logger.warning(
+                "%s is defined in more than one place, using the value from %s",
+                key,
+                origin[key],
             )
 
+    # In place: tests hold a reference to this dict.
+    FRIGATE_ENV_VARS.clear()
+    FRIGATE_ENV_VARS.update(merged)
+
+
+def _load_credentials_dir() -> dict[str, str]:
+    """Read FRIGATE_* files from the Docker or systemd credentials directory."""
+    directory = os.environ.get("CREDENTIALS_DIRECTORY", "/run/secrets")
+    values: dict[str, str] = {}
+
+    if not (os.path.isdir(directory) and os.access(directory, os.R_OK)):
+        return values
+
+    for name in os.listdir(directory):
+        if not name.startswith("FRIGATE_"):
+            continue
+
+        try:
+            values[name] = Path(os.path.join(directory, name)).read_text().strip()
+        except (OSError, UnicodeDecodeError):
+            logger.warning("Unable to read %s in %s, skipping", name, directory)
+
+    return values
+
+
+def _secrets_file_path() -> str | None:
+    """Locate secrets.yaml next to the config file."""
+    config_file = os.environ.get("CONFIG_FILE")
+    config_dir = os.path.dirname(config_file) if config_file else CONFIG_DIR
+
+    for name in ("secrets.yaml", "secrets.yml"):
+        path = os.path.join(config_dir, name)
+
+        if os.path.isfile(path):
+            return path
+
+    return None
+
+
+def _load_secrets_file() -> dict[str, str]:
+    """Read the flat FRIGATE_* map from secrets.yaml, if it exists."""
+    path = _secrets_file_path()
+
+    if path is None:
+        return {}
+
+    try:
+        with open(path) as f:
+            raw: Any = YAML(typ="safe").load(f)
+    except OSError as err:
+        raise ValueError(f"Unable to read {path}: {err.strerror}") from err
+    except YAMLError as err:
+        # The parser message can quote values, so only name a position.
+        mark = getattr(err, "problem_mark", None)
+        where = f" near line {mark.line + 1}" if mark is not None else ""
+        raise ValueError(f"{path} is not valid YAML{where}") from err
+
+    if raw is None:
+        return {}
+
+    if not isinstance(raw, dict):
+        raise ValueError(f"{path} must be a flat map of names to values")
+
+    values: dict[str, str] = {}
+
+    for key, value in raw.items():
+        name = str(key)
+
+        if isinstance(value, (dict, list)):
+            raise ValueError(f"{path} value for {name} must be a single value")
+
+        if not name.startswith("FRIGATE_"):
+            logger.warning(
+                "Ignoring %s in %s, names must start with FRIGATE_", name, path
+            )
+            continue
+
+        values[name] = "" if value is None else str(value)
+
+    return values
+
+
+def reload_sources(warn: bool = True) -> None:
+    """Re-read the file backed sources and rebuild the namespace."""
+    _CREDENTIALS_DIR.clear()
+    _CREDENTIALS_DIR.update(_load_credentials_dir())
+
+    try:
+        secrets = _load_secrets_file()
+    except ValueError as err:
+        # Keep the last good values; this runs at import and on every parse.
+        logger.error("Ignoring secrets file, %s", err)
+    else:
+        _SECRETS_FILE.clear()
+        _SECRETS_FILE.update(secrets)
+
+    _rebuild(warn)
+
+
+def apply_config_env_vars(values: Mapping[str, object]) -> None:
+    """Install the environment_vars block as the lowest priority source.
+
+    Unprefixed keys only set os.environ.
+    """
+    for key, value in values.items():
+        resolved = str(value)
+
+        if key.startswith("FRIGATE_"):
+            _CONFIG_ENV_VARS[key] = resolved
+        else:
+            os.environ[key] = resolved
+
+    _rebuild()
+
+    # Export the winning value; auth reads FRIGATE_JWT_SECRET from os.environ.
+    for key in values:
+        if key.startswith("FRIGATE_"):
+            os.environ[key] = FRIGATE_ENV_VARS[key]
+
+
+reload_sources(warn=False)
+
 
 # Matches a FRIGATE_* identifier following an opening brace.
 _FRIGATE_IDENT_RE = re.compile(r"FRIGATE_[A-Za-z0-9_]+")
@@ -29,12 +202,13 @@ def substitute_frigate_vars(value: str) -> str:
 
     * `{{` and `}}` collapse to literal `{` / `}` (the documented escape).
     * `{FRIGATE_NAME}` is replaced from `FRIGATE_ENV_VARS`; an unknown name
-      raises `KeyError` to preserve the existing "Invalid substitution"
-      error path.
+      raises `UnknownVariableError` to preserve the existing "Invalid
+      substitution" error path.
     * A `{` that begins `{FRIGATE_` but is not a well-formed
       `{FRIGATE_NAME}` placeholder raises `ValueError` (malformed
-      placeholder). Callers that catch `KeyError` to allow unknown-var
-      passthrough will still surface malformed syntax as an error.
+      placeholder). Callers that catch `UnknownVariableError` to allow
+      unknown-var passthrough will still surface malformed syntax as an
+      error.
     * Any other `{` or `}` is treated as a literal and passed through.
     """
     out: list[str] = []
@@ -58,7 +232,10 @@ def substitute_frigate_vars(value: str) -> str:
                 ):
                     key = ident_match.group(0)
                     if key not in FRIGATE_ENV_VARS:
-                        raise KeyError(key)
+                        raise UnknownVariableError(
+                            f"{key} is not defined in the environment, "
+                            "secrets.yaml, or the environment_vars config"
+                        )
                     out.append(FRIGATE_ENV_VARS[key])
                     i = ident_match.end() + 1
                     continue
@@ -94,10 +271,7 @@ EnvString = Annotated[str, AfterValidator(validate_env_string)]
 
 def validate_env_vars(v: dict[str, str], info: ValidationInfo) -> dict[str, str]:
     if isinstance(info.context, dict) and info.context.get("install", False):
-        for k, val in v.items():
-            os.environ[k] = val
-            if k.startswith("FRIGATE_"):
-                FRIGATE_ENV_VARS[k] = val
+        apply_config_env_vars(v)
 
     return v
 

frigate/config/profile_manager.py

@@ -8,6 +8,7 @@ from datetime import UTC, datetime
 from pathlib import Path
 from typing import Any
 
+from frigate.config.camera.birdseye import birdseye_modes_to_mqtt_payload
 from frigate.config.camera.updater import (
     CameraConfigUpdateEnum,
     CameraConfigUpdatePublisher,
@@ -42,8 +43,12 @@ SECTION_STATE_TOPICS: dict[str, list[tuple[str, Callable[[Any], Any]]]] = {
     "birdseye": [
         ("birdseye", lambda c: "ON" if c.birdseye.enabled else "OFF"),
         (
-            "birdseye_mode",
-            lambda c: c.birdseye.mode.value.upper() if c.birdseye.enabled else "OFF",
+            "birdseye_modes",
+            lambda c: (
+                birdseye_modes_to_mqtt_payload(c.birdseye.modes)
+                if c.birdseye.enabled
+                else "OFF"
+            ),
         ),
     ],
     "detect": [("detect", lambda c: "ON" if c.detect.enabled else "OFF")],

frigate/const.py

@@ -23,6 +23,12 @@ SHM_FRAMES_VAR = "SHM_MAX_FRAMES"
 
 REDACTED_CREDENTIAL_SENTINEL = "__FRIGATE_SAVED_CREDENTIAL__"
 
+# Stream type constants
+
+STREAM_TYPE_MAIN = "main"
+STREAM_TYPE_SUB = "sub"
+SUB_CACHE_TAG = "@sub"
+
 # Attribute & Object constants
 
 DEFAULT_ATTRIBUTE_LABEL_MAP = {
@@ -44,11 +50,7 @@ DEFAULT_ATTRIBUTE_LABEL_MAP = {
         "ups",
         "usps",
     ],
-    "truck": ["license_plate"],
-    "garbage_truck": ["license_plate"],
     "motorcycle": ["license_plate"],
-    "bus": ["license_plate"],
-    "school_bus": ["license_plate"],
 }
 ATTRIBUTE_LABEL_DISPLAY_MAP = {
     "amazon": "Amazon",

frigate/data_processing/common/license_plate/mixin.py

@@ -72,7 +72,7 @@ class LicensePlateProcessingMixin:
         # Object config
         self.lp_objects: list[str] = []
 
-        for obj, attributes in self.config.model.attributes_map.items():
+        for obj, attributes in self.config.all_attributes_map.items():
             if "license_plate" in attributes:
                 self.lp_objects.append(obj)
 
@@ -1290,7 +1290,7 @@ class LicensePlateProcessingMixin:
                 and obj_data.get("label") != "license_plate"
             ):
                 logger.debug(
-                    f"{camera}: Not a processing license plate for {obj_data.get('label', 'unknown')}."
+                    f"{camera}: Not a processing license plate for non car/motorcycle object."
                 )
                 return
 
@@ -1367,7 +1367,7 @@ class LicensePlateProcessingMixin:
 
                 if not license_plate:
                     logger.debug(
-                        f"{camera}: Detected no license plates for {obj_data.get('label', 'unknown')} object."
+                        f"{camera}: Detected no license plates for car/motorcycle object."
                     )
                     return
 

frigate/data_processing/post/audio_transcription.py

@@ -83,6 +83,10 @@ class AudioTranscriptionPostProcessor(PostProcessorApi):
         """
         event_id = data["event_id"]
         camera_name = data["camera"]
+        camera_config = self.config.cameras.get(camera_name)
+
+        if camera_config is None:
+            return
 
         if data_type == PostProcessDataEnum.recording:
             start_ts = data["frame_time"]
@@ -104,7 +108,7 @@ class AudioTranscriptionPostProcessor(PostProcessorApi):
 
         try:
             audio_data = get_audio_from_recording(
-                self.config.cameras[camera_name].ffmpeg,
+                camera_config.ffmpeg,
                 camera_name,
                 start_ts,
                 end_ts,

frigate/data_processing/post/object_descriptions.py

@@ -151,7 +151,12 @@ class ObjectDescriptionProcessor(PostProcessorApi):
             logger.error(f"Event {event_id} not found for description regeneration")
             return
 
-        camera_config = self.config.cameras[str(event.camera)]
+        camera_config = self.config.cameras.get(str(event.camera))
+
+        if camera_config is None:
+            logger.error("Camera %s no longer exists", event.camera)
+            return
+
         if not camera_config.objects.genai.enabled and not force:
             logger.error(f"GenAI not enabled for camera {event.camera}")
             return

frigate/data_processing/post/review_descriptions.py

@@ -137,7 +137,10 @@ class ReviewDescriptionProcessor(PostProcessorApi):
             return
 
         camera = data["after"]["camera"]
-        camera_config = self.config.cameras[camera]
+        camera_config = self.config.cameras.get(camera)
+
+        if camera_config is None:
+            return
 
         if not camera_config.review.genai.enabled:
             return
@@ -231,8 +234,8 @@ class ReviewDescriptionProcessor(PostProcessorApi):
                     final_data,
                     thumbs,
                     camera_config.review.genai,
-                    list(self.config.model.merged_labelmap.values()),
-                    self.config.model.all_attributes,
+                    sorted(self.config.all_labels),
+                    self.config.all_attributes,
                 ),
             ).start()
 

frigate/db/sqlitevecq.py

@@ -1,10 +1,8 @@
 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__)
@@ -19,7 +17,6 @@ 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:
@@ -56,22 +53,6 @@ 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.
 
@@ -82,17 +63,17 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase):
             return
 
         # the embeddings tables are only created once semantic search has run
-        if not self._table_exists(table):
+        cursor = self.execute_sql(
+            "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])
-
-        # 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)
+        self.execute_sql(f"DELETE FROM {table} WHERE id IN ({ids})", event_ids)
 
     def delete_embeddings_thumbnail(self, event_ids: list[str]) -> None:
         self._delete_embeddings("vec_thumbnails", event_ids)
@@ -100,67 +81,25 @@ 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:
-        for table in ("vec_descriptions", "vec_thumbnails"):
-            self._restore_vec_info_table(table)
-            self.execute_write(f"DROP TABLE IF EXISTS {table}")
+        self.execute_sql("""
+            DROP TABLE vec_descriptions;
+        """)
+        self.execute_sql("""
+            DROP TABLE vec_thumbnails;
+        """)
 
     def create_embeddings_tables(self) -> None:
         """Create vec0 virtual table for embeddings"""
-        self.execute_write("""
+        self.execute_sql("""
             CREATE VIRTUAL TABLE IF NOT EXISTS vec_thumbnails USING vec0(
                 id TEXT PRIMARY KEY,
                 thumbnail_embedding FLOAT[768] distance_metric=cosine
             );
         """)
-        self.execute_write("""
+        self.execute_sql("""
             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/detection_runners.py

@@ -25,31 +25,25 @@ def is_arm64_platform() -> bool:
     return machine in ("aarch64", "arm64", "armv8", "armv7l")
 
 
-def get_ort_session_options(model_type: str | None = None) -> ort.SessionOptions | None:
+def get_ort_session_options(
+    is_complex_model: bool = False,
+) -> ort.SessionOptions | None:
     """Get ONNX Runtime session options with appropriate settings.
 
     Args:
-        model_type: Model being loaded, used to pin its graph optimization level.
+        is_complex_model: Whether the model needs basic optimization to avoid graph fusion issues.
 
     Returns:
-        SessionOptions with a pinned optimization level, or None for default settings.
+        SessionOptions with appropriate optimization level, or None for default settings.
     """
-    # Import here to avoid circular imports
-    from frigate.embeddings.types import EnrichmentModelTypeEnum
+    if is_complex_model:
+        sess_options = ort.SessionOptions()
+        sess_options.graph_optimization_level = (
+            ort.GraphOptimizationLevel.ORT_ENABLE_BASIC
+        )
+        return sess_options
 
-    if model_type == EnrichmentModelTypeEnum.jina_v2.value:
-        # below EXTENDED the CUDA EP returns an identical vector for every image,
-        # and ORT_ENABLE_ALL fails to build on CPU with a SimplifiedLayerNormFusion error
-        level = ort.GraphOptimizationLevel.ORT_ENABLE_EXTENDED
-    elif model_type == EnrichmentModelTypeEnum.jina_v1.value:
-        # aggressive optimizations create or expect nodes that don't exist
-        level = ort.GraphOptimizationLevel.ORT_ENABLE_BASIC
-    else:
-        return None
-
-    sess_options = ort.SessionOptions()
-    sess_options.graph_optimization_level = level
-    return sess_options
+    return None
 
 
 # Import OpenVINO only when needed to avoid circular dependencies
@@ -121,6 +115,21 @@ class BaseModelRunner(ABC):
 class ONNXModelRunner(BaseModelRunner):
     """Run ONNX models using ONNX Runtime."""
 
+    @staticmethod
+    def is_cpu_complex_model(model_type: str) -> bool:
+        """Check if model needs basic optimization level to avoid graph fusion issues.
+
+        Some models (like Jina-CLIP) have issues with aggressive optimizations like
+        SimplifiedLayerNormFusion that create or expect nodes that don't exist.
+        """
+        # Import here to avoid circular imports
+        from frigate.embeddings.types import EnrichmentModelTypeEnum
+
+        return model_type in [
+            EnrichmentModelTypeEnum.jina_v1.value,
+            EnrichmentModelTypeEnum.jina_v2.value,
+        ]
+
     @staticmethod
     def is_migraphx_complex_model(model_type: str) -> bool:
         # Import here to avoid circular imports
@@ -199,20 +208,15 @@ class CudaGraphRunner(BaseModelRunner):
             EnrichmentModelTypeEnum.yolov9_license_plate.value,
         ]
 
-    # ORT performs two regular runs before it starts capturing, but on some
-    # driver / cuDNN combinations the arena still has to extend on the run that
-    # captures, and cudaMalloc is not allowed during capture. Running with
-    # capture disabled first keeps those allocations outside of the capture.
-    GRAPH_FREE_WARMUP_RUNS = 2
-
     def __init__(self, session: ort.InferenceSession, cuda_device_id: int):
         self._session = session
         self._cuda_device_id = cuda_device_id
-        self._prepared = False
+        self._captured = False
         self._io_binding: ort.IOBinding | None = None
         self._input_name: str | None = None
         self._output_names: list[str] | None = None
         self._input_ortvalue: ort.OrtValue | None = None
+        self._output_ortvalues: ort.OrtValue | None = None
 
     def get_input_names(self) -> list[str]:
         """Get input names for the model."""
@@ -222,41 +226,35 @@ class CudaGraphRunner(BaseModelRunner):
         """Get the input width of the model."""
         return self._session.get_inputs()[0].shape[3]
 
-    def _prepare(self, input_name: str, tensor_input: np.ndarray) -> None:
-        """Bind CUDA buffers and warm the session up with capture disabled."""
-        self._io_binding = self._session.io_binding()
-        self._input_name = input_name
-        self._output_names = [o.name for o in self._session.get_outputs()]
-
-        self._input_ortvalue = ort.OrtValue.ortvalue_from_numpy(
-            tensor_input, "cuda", self._cuda_device_id
-        )
-        self._io_binding.bind_ortvalue_input(self._input_name, self._input_ortvalue)
-
-        for name in self._output_names:
-            # Bind outputs to CUDA and allow ORT to allocate appropriately
-            self._io_binding.bind_output(name, "cuda", self._cuda_device_id)
-
-        # gpu_graph_id -1 disables capture and replay for the run
-        warmup_options = ort.RunOptions()
-        warmup_options.add_run_config_entry("gpu_graph_id", "-1")
-
-        for _ in range(self.GRAPH_FREE_WARMUP_RUNS):
-            self._session.run_with_iobinding(self._io_binding, warmup_options)
-
-        self._prepared = True
-
     def run(self, input: dict[str, Any]):
         # Extract the single tensor input (assuming one input)
         input_name = list(input.keys())[0]
-        tensor_input = np.ascontiguousarray(input[input_name])
+        tensor_input = input[input_name]
+        tensor_input = np.ascontiguousarray(tensor_input)
 
-        if not self._prepared:
-            self._prepare(input_name, tensor_input)
-        else:
-            # Replay using updated input
-            self._input_ortvalue.update_inplace(tensor_input)
+        if not self._captured:
+            # Prepare IOBinding with CUDA buffers and let ORT allocate outputs on device
+            self._io_binding = self._session.io_binding()
+            self._input_name = input_name
+            self._output_names = [o.name for o in self._session.get_outputs()]
 
+            self._input_ortvalue = ort.OrtValue.ortvalue_from_numpy(
+                tensor_input, "cuda", self._cuda_device_id
+            )
+            self._io_binding.bind_ortvalue_input(self._input_name, self._input_ortvalue)
+
+            for name in self._output_names:
+                # Bind outputs to CUDA and allow ORT to allocate appropriately
+                self._io_binding.bind_output(name, "cuda", self._cuda_device_id)
+
+            # First IOBinding run to allocate, execute, and capture CUDA Graph
+            ro = ort.RunOptions()
+            self._session.run_with_iobinding(self._io_binding, ro)
+            self._captured = True
+            return self._io_binding.copy_outputs_to_cpu()
+
+        # Replay using updated input, copy results to CPU
+        self._input_ortvalue.update_inplace(tensor_input)
         ro = ort.RunOptions()
         self._session.run_with_iobinding(self._io_binding, ro)
         return self._io_binding.copy_outputs_to_cpu()
@@ -325,12 +323,6 @@ class OpenVINOModelRunner(BaseModelRunner):
         if device in ["GPU", "AUTO", "NPU"]:
             self.ov_core.set_property(device, {"PERFORMANCE_HINT": "LATENCY"})
 
-        if device in ["GPU", "AUTO"]:
-            try:
-                self.ov_core.set_property("GPU", {"GPU_QUEUE_THROTTLE": "LOW"})
-            except Exception as e:
-                logger.debug(f"GPU_QUEUE_THROTTLE not supported: {e}")
-
         if device == "NPU" and OpenVINOModelRunner.is_detection_model(model_type):
             try:
                 self.ov_core.set_property(device, {"NPU_TURBO": "YES"})
@@ -634,7 +626,9 @@ def get_optimized_runner(
     return ONNXModelRunner(
         ort.InferenceSession(
             model_path,
-            sess_options=get_ort_session_options(model_type),
+            sess_options=get_ort_session_options(
+                ONNXModelRunner.is_cpu_complex_model(model_type)
+            ),
             providers=providers,
             provider_options=options,
         ),

frigate/detectors/detector_config.py

@@ -3,7 +3,7 @@ import json
 import logging
 import os
 from enum import Enum
-from typing import Any
+from typing import Any, ClassVar
 
 import requests
 from pydantic import BaseModel, ConfigDict, Field
@@ -15,6 +15,9 @@ from frigate.util.builtin import generate_color_palette, load_labels
 
 logger = logging.getLogger(__name__)
 
+# attributes that are recognized rather than shown as a logo
+NON_LOGO_ATTRIBUTES = ["face", "license_plate"]
+
 
 class PixelFormatEnum(str, Enum):
     rgb = "rgb"
@@ -44,7 +47,27 @@ class ModelTypeEnum(str, Enum):
     yologeneric = "yolo-generic"
 
 
+class SceneEnum(str, Enum):
+    """The camera environment a detection model is intended for."""
+
+    all = "all"
+    indoor = "indoor"
+    outdoor = "outdoor"
+    indoor_thermal = "indoor_thermal"
+    outdoor_thermal = "outdoor_thermal"
+
+
 class ModelConfig(BaseModel):
+    scene: SceneEnum = Field(
+        default=SceneEnum.all,
+        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.",
+    )
+    devices: list[str] = Field(
+        default_factory=list,
+        title="Detection hardware",
+        description="Hardware this model runs on, as '<detector>' or '<detector>:<device>' (for example 'edgetpu:pci:0' or 'openvino:GPU'). Listing the same device more than once runs additional inference processes on it.",
+    )
     path: str | None = Field(
         None,
         title="Custom object detector model path",
@@ -111,7 +134,7 @@ class ModelConfig(BaseModel):
 
     @property
     def non_logo_attributes(self) -> list[str]:
-        return ["face", "license_plate"]
+        return NON_LOGO_ATTRIBUTES
 
     @property
     def all_attributes(self) -> list[str]:
@@ -201,9 +224,7 @@ class ModelConfig(BaseModel):
             unique_attributes.update(attributes)
 
         self._all_attributes = list(unique_attributes)
-        self._all_attribute_logos = list(
-            unique_attributes - set(["face", "license_plate"])
-        )
+        self._all_attribute_logos = list(unique_attributes - set(NON_LOGO_ATTRIBUTES))
 
         self._merged_labelmap = {
             **{int(key): val for key, val in model_info["labelMap"].items()},
@@ -234,6 +255,14 @@ class ModelConfig(BaseModel):
 
 
 class BaseDetectorConfig(BaseModel):
+    # how the trailing part of a device string ("openvino:GPU" -> "GPU") maps onto
+    # this detector's fields, and whether the same device may be listed more than
+    # once to run additional inference processes against it. Most accelerators
+    # multiplex fine, so this is opt-out rather than opt-in.
+    device_spec_field: ClassVar[str] = "device"
+    device_spec_type: ClassVar[type] = str
+    shareable: ClassVar[bool] = True
+
     # the type field must be defined in all subclasses
     type: str = Field(
         default="cpu",

frigate/detectors/detector_types.py

@@ -2,7 +2,7 @@ import importlib
 import logging
 import pkgutil
 from enum import Enum
-from typing import Annotated, Union
+from typing import Annotated, Union, get_args
 
 from pydantic import Field
 
@@ -39,3 +39,21 @@ DetectorConfig = Annotated[
     Union[tuple(BaseDetectorConfig.__subclasses__())],  # noqa: UP007
     Field(discriminator="type"),
 ]
+
+
+def _discriminator_value(config_class: type[BaseDetectorConfig]) -> str | None:
+    """Read the Literal value of a detector config class' type field."""
+    field = config_class.model_fields.get("type")
+
+    if field is None:
+        return None
+
+    values = get_args(field.annotation)
+    return values[0] if values else None
+
+
+config_types: dict[str, type[BaseDetectorConfig]] = {
+    key: config_class
+    for config_class in BaseDetectorConfig.__subclasses__()
+    if (key := _discriminator_value(config_class)) is not None
+}

frigate/detectors/device.py

@@ -0,0 +1,113 @@
+"""Parsing of detection hardware device strings."""
+
+import logging
+from dataclasses import dataclass
+
+from pydantic import TypeAdapter, ValidationError
+
+from frigate.detectors.detector_config import BaseDetectorConfig, ModelConfig
+from frigate.detectors.detector_types import DetectorConfig, config_types
+
+logger = logging.getLogger(__name__)
+
+_detector_adapter: TypeAdapter[BaseDetectorConfig] = TypeAdapter(DetectorConfig)
+
+
+@dataclass(frozen=True)
+class DeviceSpec:
+    """A parsed `<detector>` or `<detector>:<device>` string."""
+
+    raw: str
+    detector: str
+    device: str | None
+
+    @property
+    def shareable(self) -> bool:
+        """Whether this device may be listed more than once."""
+        return config_types[self.detector].shareable
+
+
+class DeviceParseError(ValueError):
+    pass
+
+
+def parse_device(raw: str) -> DeviceSpec:
+    """Parse a device string into its detector type and detector specific device.
+
+    Args:
+        raw: The configured device string, for example 'edgetpu:pci:0'
+
+    Returns:
+        The parsed spec
+
+    Raises:
+        DeviceParseError: If the detector type is unknown or the device is not
+            valid for that detector
+    """
+    detector, separator, device = raw.partition(":")
+
+    if detector not in config_types:
+        raise DeviceParseError(
+            f"'{raw}' does not name a known detector. Available detectors are {', '.join(sorted(config_types))}"
+        )
+
+    spec = DeviceSpec(raw=raw, detector=detector, device=device if separator else None)
+
+    # surface a bad device now rather than when the detection process starts
+    build_detector_config(spec, None)
+    return spec
+
+
+def build_detector_config(
+    spec: DeviceSpec, model: ModelConfig | None
+) -> BaseDetectorConfig:
+    """Build the detector config a device string describes.
+
+    Args:
+        spec: The parsed device spec
+        model: The model this detector runs, if it has been resolved yet
+
+    Returns:
+        The validated detector config
+
+    Raises:
+        DeviceParseError: If the device is not valid for this detector type
+    """
+    config: dict[str, object] = {"type": spec.detector, "model": model}
+
+    if spec.device is not None:
+        config_class = config_types[spec.detector]
+
+        try:
+            config[config_class.device_spec_field] = config_class.device_spec_type(
+                spec.device
+            )
+        except ValueError as err:
+            raise DeviceParseError(
+                f"'{spec.raw}' is not a valid {spec.detector} device: {err}"
+            ) from err
+
+    try:
+        return _detector_adapter.validate_python(config)
+    except ValidationError as err:
+        raise DeviceParseError(f"'{spec.raw}' is not a valid device: {err}") from err
+
+
+def runner_names(devices: list[DeviceSpec]) -> list[str]:
+    """Build a unique name for each device, since a shareable device may repeat.
+
+    Args:
+        devices: Every device spec across every configured model, in config order
+
+    Returns:
+        A name per device, suffixed with '#2', '#3', etc. on repeats
+    """
+    names: list[str] = []
+    seen: dict[str, int] = {}
+
+    for spec in devices:
+        count = seen.get(spec.raw, 0) + 1
+        seen[spec.raw] = count
+        names.append(spec.raw if count == 1 else f"{spec.raw}#{count}")
+
+    return names

frigate/detectors/hardware.py

@@ -0,0 +1,368 @@
+"""Discovery of object detection hardware attached to the system.
+
+Every probe here is a filesystem read. Nothing shells out, initializes a
+runtime, or opens a device, so this is cheap enough to run from the API process
+while detector children hold the hardware.
+
+Hardware is reported whether or not this image ships a detector that can drive
+it. Matching hardware to an image is a separate concern.
+"""
+
+import logging
+import os
+from glob import glob
+
+from pydantic import BaseModel, Field
+
+from frigate.const import SUPPORTED_RK_SOCS
+from frigate.detectors.detector_types import config_types
+from frigate.util.services import enumerate_drm_devices
+
+logger = logging.getLogger(__name__)
+
+# roots the probes read from, so tests can point them at a fixture tree
+SYS_ROOT = "/sys"
+DEV_ROOT = "/dev"
+PROC_ROOT = "/proc"
+ETC_ROOT = "/etc"
+
+# a Coral reports as Global Unichip until its firmware is loaded, then as Google
+CORAL_USB_IDS = {("1a6e", "089a"), ("18d1", "9302")}
+
+INTEL_DRM_DRIVERS = ("i915", "xe")
+AMD_DRM_DRIVERS = ("amdgpu",)
+
+
+class HardwareUnit(BaseModel):
+    """One physical piece of hardware."""
+
+    device: str = Field(
+        title="Device string",
+        description="The value to put in a model's devices list, for example 'edgetpu:pci:1'.",
+    )
+    label: str = Field(
+        title="Unit label",
+        description="How to identify this unit among others of the same kind, for example 'PCIe 1'.",
+    )
+
+
+class DetectionHardware(BaseModel):
+    """A kind of detection hardware, and every unit of it that was found."""
+
+    key: str = Field(
+        title="Hardware key",
+        description="Stable identifier for this kind of hardware.",
+    )
+    detector: str = Field(
+        title="Detector type",
+        description="The detector that drives this hardware.",
+    )
+    name: str = Field(
+        title="Hardware name",
+        description="Human readable name for this kind of hardware.",
+    )
+    units: list[HardwareUnit] = Field(
+        title="Units",
+        description="Each physical piece of this hardware that was found.",
+    )
+    count: int = Field(
+        title="Unit count",
+        description="How many units were found.",
+    )
+    unlimited: bool = Field(
+        title="Unlimited detectors",
+        description="Whether this hardware can run more inference processes than there are units.",
+    )
+
+
+def _read(path: str) -> str | None:
+    """Read a small file, returning None if it cannot be read."""
+    try:
+        with open(path) as f:
+            return f.read().strip()
+    except OSError:
+        return None
+
+
+def _is_shareable(detector: str) -> bool:
+    """Whether a detector lets the same device run more than one process."""
+    config_class = config_types.get(detector)
+
+    # a detector missing from this image is assumed to behave like most of them
+    return config_class.shareable if config_class else True
+
+
+def _hardware(
+    key: str, detector: str, name: str, units: list[HardwareUnit]
+) -> DetectionHardware:
+    return DetectionHardware(
+        key=key,
+        detector=detector,
+        name=name,
+        units=units,
+        count=len(units),
+        unlimited=_is_shareable(detector),
+    )
+
+
+def detect_coral_pci() -> DetectionHardware | None:
+    """Find PCIe and M.2 Coral accelerators, which register as apex devices."""
+    names = sorted(
+        os.path.basename(path) for path in glob(f"{SYS_ROOT}/class/apex/apex_*")
+    )
+
+    if not names:
+        return None
+
+    units = [
+        HardwareUnit(device=f"edgetpu:pci:{index}", label=f"PCIe {index}")
+        for index in range(len(names))
+    ]
+    return _hardware("edgetpu:pci", "edgetpu", "Coral EdgeTPU (PCIe)", units)
+
+
+def detect_coral_usb() -> DetectionHardware | None:
+    """Find USB Coral accelerators by their USB vendor and product ids."""
+    found = 0
+
+    for device_dir in sorted(glob(f"{SYS_ROOT}/bus/usb/devices/*")):
+        vendor = _read(os.path.join(device_dir, "idVendor"))
+        product = _read(os.path.join(device_dir, "idProduct"))
+
+        if vendor and product and (vendor.lower(), product.lower()) in CORAL_USB_IDS:
+            found += 1
+
+    if not found:
+        return None
+
+    units = [
+        HardwareUnit(device=f"edgetpu:usb:{index}", label=f"USB {index}")
+        for index in range(found)
+    ]
+    return _hardware("edgetpu:usb", "edgetpu", "Coral EdgeTPU (USB)", units)
+
+
+def _drm_devices(drivers: tuple[str, ...]) -> list[str]:
+    """PCI addresses of DRM devices bound to one of the given drivers."""
+    return sorted(
+        pdev for pdev, driver in enumerate_drm_devices().items() if driver in drivers
+    )
+
+
+def detect_intel_gpu() -> DetectionHardware | None:
+    """Find Intel GPUs through their DRM driver."""
+    pdevs = _drm_devices(INTEL_DRM_DRIVERS)
+
+    if not pdevs:
+        return None
+
+    # OpenVINO reports a lone GPU as "GPU" and enumerates them as GPU.0, GPU.1
+    # only when there is more than one
+    if len(pdevs) == 1:
+        units = [HardwareUnit(device="openvino:GPU", label=pdevs[0])]
+    else:
+        units = [
+            HardwareUnit(device=f"openvino:GPU.{index}", label=pdev)
+            for index, pdev in enumerate(pdevs)
+        ]
+
+    return _hardware("openvino:GPU", "openvino", "Intel GPU", units)
+
+
+def detect_intel_npu() -> DetectionHardware | None:
+    """Find Intel NPUs, which register as accel devices bound to intel_vpu."""
+    units = []
+
+    for accel_path in sorted(glob(f"{SYS_ROOT}/class/accel/accel*")):
+        try:
+            driver = os.path.basename(os.readlink(f"{accel_path}/device/driver"))
+        except OSError:
+            continue
+
+        if driver != "intel_vpu":
+            continue
+
+        units.append(
+            HardwareUnit(device="openvino:NPU", label=os.path.basename(accel_path))
+        )
+
+    if not units:
+        return None
+
+    # OpenVINO has no way to address a specific NPU, so only the first is usable
+    return _hardware("openvino:NPU", "openvino", "Intel NPU", units[:1])
+
+
+def detect_amd_gpu() -> DetectionHardware | None:
+    """Find AMD GPUs through their DRM driver."""
+    pdevs = _drm_devices(AMD_DRM_DRIVERS)
+
+    if not pdevs:
+        return None
+
+    # ROCm runs through onnx, whose MIGraphX provider takes no device index, so
+    # only one is addressable
+    units = [HardwareUnit(device="onnx", label=pdevs[0])]
+    return _hardware("onnx:amd", "onnx", "AMD GPU", units)
+
+
+def detect_nvidia_gpu() -> DetectionHardware | None:
+    """Find discrete Nvidia GPUs through the nvidia driver's proc entries."""
+    units = []
+
+    for index, gpu_dir in enumerate(sorted(glob(f"{PROC_ROOT}/driver/nvidia/gpus/*"))):
+        information = _read(os.path.join(gpu_dir, "information")) or ""
+        name = f"GPU {index}"
+
+        for line in information.splitlines():
+            if line.startswith("Model:"):
+                name = line.split(":", 1)[1].strip()
+                break
+
+        units.append(HardwareUnit(device=f"onnx:{index}", label=name))
+
+    if not units:
+        return None
+
+    # the model name is more useful as the hardware name when there is only one
+    name = units[0].label if len(units) == 1 else "NVIDIA GPU"
+    return _hardware("onnx:nvidia", "onnx", name, units)
+
+
+def detect_jetson() -> DetectionHardware | None:
+    """Find an Nvidia Jetson, whose integrated GPU runs through tensorrt."""
+    is_jetson = os.path.isfile(f"{ETC_ROOT}/nv_tegra_release") or os.path.exists(
+        f"{SYS_ROOT}/devices/gpu.0/load"
+    )
+
+    if not is_jetson:
+        return None
+
+    units = [HardwareUnit(device="tensorrt:0", label="Integrated GPU")]
+    return _hardware("tensorrt", "tensorrt", "NVIDIA Jetson", units)
+
+
+def _dev_units(pattern: str, device: str, label: str) -> list[HardwareUnit]:
+    """Build units from device nodes matching a glob."""
+    return [
+        HardwareUnit(device=device.format(index=index), label=f"{label} {index}")
+        for index in range(len(glob(f"{DEV_ROOT}/{pattern}")))
+    ]
+
+
+def detect_hailo() -> DetectionHardware | None:
+    """Find Hailo accelerators by their device nodes."""
+    nodes = sorted(glob(f"{DEV_ROOT}/hailo*"))
+
+    if not nodes:
+        return None
+
+    # the hailo runtime schedules across every attached device itself, so there
+    # is nothing to address individually
+    units = [HardwareUnit(device="hailo8l:PCIe", label=os.path.basename(nodes[0]))]
+    return _hardware("hailo8l", "hailo8l", "Hailo", units)
+
+
+def detect_memryx() -> DetectionHardware | None:
+    """Find MemryX accelerators by their device nodes."""
+    units = _dev_units("memx*", "memryx:PCIe:{index}", "PCIe")
+
+    if not units:
+        return None
+
+    return _hardware("memryx", "memryx", "MemryX MX3", units)
+
+
+def detect_rockchip() -> DetectionHardware | None:
+    """Find a Rockchip NPU by reading the SoC from the device tree."""
+    compatible = _read(f"{PROC_ROOT}/device-tree/compatible")
+
+    if not compatible:
+        return None
+
+    soc = compatible.split(",")[-1].strip("\x00")
+
+    if soc not in SUPPORTED_RK_SOCS:
+        return None
+
+    units = [HardwareUnit(device="rknn", label=soc.upper())]
+    return _hardware("rknn", "rknn", f"Rockchip NPU ({soc.upper()})", units)
+
+
+def detect_axengine() -> DetectionHardware | None:
+    """Find an AXERA accelerator by its control device node."""
+    if not os.path.exists(f"{DEV_ROOT}/axcl_host"):
+        return None
+
+    units = [HardwareUnit(device="axengine", label="AXERA")]
+    return _hardware("axengine", "axengine", "AXERA NPU", units)
+
+
+def detect_synaptics() -> DetectionHardware | None:
+    """Find a Synaptics NPU by its device node."""
+    if not os.path.exists(f"{DEV_ROOT}/synap"):
+        return None
+
+    units = [HardwareUnit(device="synaptics", label="Synaptics")]
+    return _hardware("synaptics", "synaptics", "Synaptics NPU", units)
+
+
+def detect_cpu() -> DetectionHardware:
+    """The CPU, which is always available."""
+    units = [HardwareUnit(device="cpu", label="CPU")]
+    return _hardware("cpu", "cpu", "CPU", units)
+
+
+# ordered so accelerators are offered ahead of the CPU fallback
+PROBES = (
+    detect_coral_pci,
+    detect_coral_usb,
+    detect_hailo,
+    detect_memryx,
+    detect_intel_npu,
+    detect_intel_gpu,
+    detect_nvidia_gpu,
+    detect_jetson,
+    detect_amd_gpu,
+    detect_rockchip,
+    detect_axengine,
+    detect_synaptics,
+    detect_cpu,
+)
+
+
+class HardwareProber:
+    """Probes for detection hardware, caching the result for the process."""
+
+    _hardware: list[DetectionHardware] | None = None
+
+    def probe(self, refresh: bool = False) -> list[DetectionHardware]:
+        """Get the detection hardware attached to this system.
+
+        Args:
+            refresh: Probe again instead of using the cached result
+
+        Returns:
+            Every kind of detection hardware that was found
+        """
+        if self._hardware is not None and not refresh:
+            return self._hardware
+
+        found = []
+
+        for probe in PROBES:
+            try:
+                hardware = probe()
+            except Exception:
+                logger.warning("Failed to probe for %s", probe.__name__, exc_info=True)
+                continue
+
+            if hardware is not None:
+                found.append(hardware)
+
+        logger.debug("Detected hardware: %s", [h.key for h in found])
+        self._hardware = found
+        return found
+
+
+hardware_prober = HardwareProber()

frigate/detectors/plugins/cpu_tfl.py

@@ -1,5 +1,5 @@
 import logging
-from typing import Literal
+from typing import ClassVar, Literal
 
 from pydantic import ConfigDict, Field
 
@@ -27,6 +27,9 @@ class CpuDetectorConfig(BaseDetectorConfig):
         title="CPU",
     )
 
+    device_spec_field: ClassVar[str] = "num_threads"
+    device_spec_type: ClassVar[type] = int
+
     type: Literal[DETECTOR_KEY]
     num_threads: int = Field(
         default=3,

frigate/detectors/plugins/edgetpu_tfl.py

@@ -1,7 +1,7 @@
 import logging
 import math
 import os
-from typing import Literal
+from typing import ClassVar, Literal
 
 import cv2
 import numpy as np
@@ -28,6 +28,9 @@ class EdgeTpuDetectorConfig(BaseDetectorConfig):
         title="EdgeTPU",
     )
 
+    # a TPU can only be opened by one process
+    shareable: ClassVar[bool] = False
+
     type: Literal[DETECTOR_KEY]
     device: str = Field(
         default=None,

frigate/detectors/plugins/memryx.py

@@ -5,7 +5,7 @@ import shutil
 import urllib.request
 import zipfile
 from queue import Queue
-from typing import Literal
+from typing import ClassVar, Literal
 
 import cv2
 import numpy as np
@@ -37,6 +37,9 @@ class MemryXDetectorConfig(BaseDetectorConfig):
         title="MemryX",
     )
 
+    # an accelerator can only be opened by one process
+    shareable: ClassVar[bool] = False
+
     type: Literal[DETECTOR_KEY]
     device: str = Field(
         default="PCIe",

frigate/detectors/plugins/openvino.py

@@ -28,7 +28,7 @@ class OvDetectorConfig(BaseDetectorConfig):
 
     type: Literal[DETECTOR_KEY]
     device: str = Field(
-        default=None,
+        default="AUTO",
         title="Device Type",
         description="The device to use for OpenVINO inference (e.g. 'CPU', 'GPU', 'NPU').",
     )

frigate/detectors/plugins/rknn.py

@@ -2,7 +2,7 @@ import logging
 import os.path
 import re
 import urllib.request
-from typing import Literal
+from typing import ClassVar, Literal
 
 import cv2
 import numpy as np
@@ -35,6 +35,9 @@ class RknnDetectorConfig(BaseDetectorConfig):
         title="RKNN",
     )
 
+    device_spec_field: ClassVar[str] = "num_cores"
+    device_spec_type: ClassVar[type] = int
+
     type: Literal[DETECTOR_KEY]
     num_cores: int = Field(
         default=0,

frigate/detectors/plugins/tensorrt.py

@@ -14,7 +14,7 @@ try:
 except ModuleNotFoundError:
     TRT_SUPPORT = False
 
-from typing import Literal
+from typing import ClassVar, Literal
 
 from pydantic import ConfigDict, Field
 
@@ -53,6 +53,8 @@ class TensorRTDetectorConfig(BaseDetectorConfig):
         title="TensorRT",
     )
 
+    device_spec_type: ClassVar[type] = int
+
     type: Literal[DETECTOR_KEY]
     device: int = Field(
         default=0, title="GPU Device Index", description="The GPU device index to use."

frigate/embeddings/embeddings.py

@@ -6,10 +6,9 @@ import logging
 import os
 import threading
 import time
-from typing import Any
 
 import numpy as np
-from peewee import DatabaseError, DoesNotExist, IntegrityError
+from peewee import DoesNotExist, IntegrityError
 from PIL import Image
 from playhouse.shortcuts import model_to_dict
 
@@ -208,10 +207,12 @@ class Embeddings:
         embedding = self.vision_embedding([thumbnail])[0]
 
         if upsert:
-            self.db.upsert_embeddings(
-                "vec_thumbnails",
-                "thumbnail_embedding",
-                {event_id: serialize(embedding)},
+            self.db.execute_sql(
+                """
+                INSERT OR REPLACE INTO vec_thumbnails(id, thumbnail_embedding)
+                VALUES(?, ?)
+                """,
+                (event_id, serialize(embedding)),
             )
 
         self.image_inference_speed.update(datetime.datetime.now().timestamp() - start)
@@ -250,12 +251,19 @@ class Embeddings:
         embeddings = self.vision_embedding(valid_thumbs)
 
         if upsert:
-            items = {}
+            items = []
             for i in range(len(valid_ids)):
-                items[valid_ids[i]] = serialize(embeddings[i])
+                items.append(valid_ids[i])
+                items.append(serialize(embeddings[i]))
                 self.image_eps.update()
 
-            self.db.upsert_embeddings("vec_thumbnails", "thumbnail_embedding", items)
+            self.db.execute_sql(
+                """
+                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))
@@ -269,10 +277,12 @@ class Embeddings:
         embedding = self.text_embedding([description])[0]
 
         if upsert:
-            self.db.upsert_embeddings(
-                "vec_descriptions",
-                "description_embedding",
-                {event_id: serialize(embedding)},
+            self.db.execute_sql(
+                """
+                INSERT OR REPLACE INTO vec_descriptions(id, description_embedding)
+                VALUES(?, ?)
+                """,
+                (event_id, serialize(embedding)),
             )
 
         self.text_inference_speed.update(datetime.datetime.now().timestamp() - start)
@@ -292,14 +302,19 @@ class Embeddings:
 
         if upsert:
             ids = list(event_descriptions.keys())
-            items = {}
+            items = []
 
             for i in range(len(ids)):
-                items[ids[i]] = serialize(embeddings[i])
+                items.append(ids[i])
+                items.append(serialize(embeddings[i]))
                 self.text_eps.update()
 
-            self.db.upsert_embeddings(
-                "vec_descriptions", "description_embedding", items
+            self.db.execute_sql(
+                """
+                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)
@@ -307,17 +322,6 @@ 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()
@@ -342,18 +346,14 @@ class Embeddings:
             batch_size = 32
         current_page = 1
 
-        totals.update(
-            {
-                "thumbnails": 0,
-                "descriptions": 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",
-            }
-        )
+        totals = {
+            "thumbnails": 0,
+            "descriptions": 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)
 

frigate/embeddings/maintainer.py

@@ -609,6 +609,18 @@ class EmbeddingMaintainer(threading.Thread):
                 # Embed the thumbnail
                 self._embed_thumbnail(event_id, thumbnail)
 
+            # every post processor below reads config.cameras[camera], but
+            # tracked_events still has to be released or the thumbnails held
+            # for this event leak, same as the two exits above
+            if camera not in self.config.cameras:
+                logger.debug("Skipping post processing for removed camera %s", camera)
+
+                for processor in self.post_processors:
+                    if isinstance(processor, ObjectDescriptionProcessor):
+                        processor.cleanup_event(event_id)
+
+                continue
+
             # call any defined post processors
             for processor in self.post_processors:
                 if isinstance(processor, LicensePlatePostProcessor):
@@ -666,11 +678,18 @@ class EmbeddingMaintainer(threading.Thread):
         to_remove = []
 
         for id, data in self.detected_license_plates.items():
+            camera_config = self.config.cameras.get(data["camera"])
+
+            if camera_config is None:
+                # camera was removed, drop the entry rather than expiring it
+                to_remove.append(id)
+                continue
+
             last_seen = data.get("last_seen", 0)
             if not last_seen:
                 continue
 
-            if now - last_seen > self.config.cameras[data["camera"]].lpr.expire_time:
+            if now - last_seen > camera_config.lpr.expire_time:
                 to_remove.append(id)
         for id in to_remove:
             self.event_metadata_publisher.publish(

frigate/events/audio.py

@@ -210,7 +210,11 @@ class AudioEventMaintainer(threading.Thread):
         # per-camera stop signal so a single maintainer can be torn down at
         # runtime (e.g. on camera removal) without stopping the whole process
         self.camera_stop_event = threading.Event()
-        self.detector = AudioTfl(stop_event, self.camera_config.audio.num_threads)
+        self.detector = AudioTfl(
+            stop_event,
+            self.camera_config.audio.num_threads,
+            self.camera_config.audio.labelmap,
+        )
         self.shape = (int(round(AUDIO_DURATION * AUDIO_SAMPLE_RATE)),)
         self.chunk_size = int(round(AUDIO_DURATION * AUDIO_SAMPLE_RATE * 2))
         self.logger = logging.getLogger(f"audio.{self.camera_config.name}")
@@ -392,7 +396,10 @@ class AudioEventMaintainer(threading.Thread):
 
         while not self.stop_event.is_set() and not self.camera_stop_event.is_set():
             # check if there is an updated config
-            self.config_subscriber.check_for_updates()
+            updated_topics = self.config_subscriber.check_for_updates()
+
+            if CameraConfigUpdateEnum.audio.name in updated_topics:
+                self.detector.update_labelmap(self.camera_config.audio.labelmap)
 
             enabled = self.camera_config.enabled
             if enabled != self.was_enabled:
@@ -451,10 +458,17 @@ class AudioEventMaintainer(threading.Thread):
 
 
 class AudioTfl:
-    def __init__(self, stop_event: threading.Event, num_threads: int = 2) -> None:
+    def __init__(
+        self,
+        stop_event: threading.Event,
+        num_threads: int = 2,
+        labelmap: dict[int, str] | None = None,
+    ) -> None:
         self.stop_event = stop_event
         self.num_threads = num_threads
-        self.labels = load_labels("/audio-labelmap.txt", prefill=521)
+        self._default_labels = load_labels("/audio-labelmap.txt", prefill=521)
+        self.labels: dict[int, str] = {}
+        self.update_labelmap(labelmap or {})
         # Suppress TFLite delegate creation messages that bypass Python logging
         with suppress_stderr_during("tflite_interpreter_init"):
             self.interpreter = Interpreter(
@@ -466,6 +480,10 @@ class AudioTfl:
         self.tensor_input_details = self.interpreter.get_input_details()
         self.tensor_output_details = self.interpreter.get_output_details()
 
+    def update_labelmap(self, labelmap: dict[int, str]) -> None:
+        """Merge configured label overrides into the default audio labelmap."""
+        self.labels = {**self._default_labels, **labelmap}
+
     def _detect_raw(self, tensor_input: np.ndarray) -> np.ndarray:
         self.interpreter.set_tensor(self.tensor_input_details[0]["index"], tensor_input)
         self.interpreter.invoke()
@@ -504,10 +522,14 @@ class AudioTfl:
 
         raw_detections = self._detect_raw(tensor_input)
 
+        detected_labels: set[str] = set()
+
         for d in raw_detections:
             if d[1] < threshold:
                 break
-            detections.append(
-                (self.labels[int(d[0])], float(d[1]), (d[2], d[3], d[4], d[5]))
-            )
+            label = self.labels[int(d[0])]
+            if label in detected_labels:
+                continue
+            detected_labels.add(label)
+            detections.append((label, float(d[1]), (d[2], d[3], d[4], d[5])))
         return detections

frigate/events/cleanup.py

@@ -197,9 +197,11 @@ class EventCleanup(threading.Thread):
 
     def expire_clips(self) -> list[str]:
         ## Expire events from unlisted cameras based on the global config
+        # effective days cover the sub window, keeping tracked objects in
+        # Explore while sub recordings and review items still exist
         expire_days = max(
-            self.config.record.alerts.retain.days,
-            self.config.record.detections.retain.days,
+            self.config.record.effective_alert_days,
+            self.config.record.effective_detection_days,
         )
         file_extension = None  # mp4 clips are no longer stored in /clips
         update_params = {"has_clip": False}
@@ -278,15 +280,13 @@ class EventCleanup(threading.Thread):
 
         ## Expire events from cameras based on the camera config
         for name, camera in self.config.cameras.items():
-            expire_days = max(
-                camera.record.alerts.retain.days,
-                camera.record.detections.retain.days,
-            )
+            # effective days cover the sub window, keeping tracked objects
+            # in Explore while sub recordings and review items still exist
             alert_expire_date = (
-                now - datetime.timedelta(days=camera.record.alerts.retain.days)
+                now - datetime.timedelta(days=camera.record.effective_alert_days)
             ).timestamp()
             detection_expire_date = (
-                now - datetime.timedelta(days=camera.record.detections.retain.days)
+                now - datetime.timedelta(days=camera.record.effective_detection_days)
             ).timestamp()
             # grab all events after specific time
             expired_events = (

frigate/events/maintainer.py

@@ -159,7 +159,8 @@ class EventProcessor(threading.Thread):
             if width is None or height is None:
                 return
 
-            first_detector = list(self.config.detectors.values())[0]
+            camera_model = self.config.model_for_camera(camera)
+            camera_detector = self.config.devices_for_model(camera_model)[0].detector
 
             start_time = event_data["start_time"]
             end_time = (
@@ -229,13 +230,9 @@ class EventProcessor(threading.Thread):
                 Event.thumbnail: event_data.get("thumbnail"),
                 Event.has_clip: event_data["has_clip"],
                 Event.has_snapshot: event_data["has_snapshot"],
-                Event.model_hash: first_detector.model.model_hash
-                if first_detector.model
-                else None,
-                Event.model_type: first_detector.model.model_type
-                if first_detector.model
-                else None,
-                Event.detector_type: first_detector.type,
+                Event.model_hash: camera_model.model_hash,
+                Event.model_type: camera_model.model_type,
+                Event.detector_type: camera_detector,
                 Event.data: {
                     "box": box,
                     "region": region,

frigate/ffmpeg_presets.py

@@ -121,8 +121,8 @@ PRESETS_HW_ACCEL_SCALE = {
     "preset-rpi-64-h264": "-r {0} -vf fps={0},scale={1}:{2}",
     "preset-rpi-64-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 fps={0},vpp_qsv=w={1}:h={2}:format=nv12,hwdownload,format=nv12,format=yuv420p",
-    "preset-intel-qsv-h265": "-r {0} -vf fps={0},vpp_qsv=w={1}:h={2}:format=nv12,hwdownload,format=nv12,format=yuv420p",
+    "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-h265": "-r {0} -vf vpp_qsv=w={1}:h={2}:format=nv12,hwdownload,format=nv12,fps={0},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/prompts.py

@@ -262,6 +262,10 @@ def get_tool_definitions(
     `attribute` parameter is exposed for filtering by their labels. When the
     embeddings model only understands English (JinaV1), the `semantic_query`
     description instructs the model to write the query in English.
+
+    Descriptions here stay mechanical: which tool to reach for, and how the
+    filters relate to each other, is stated once in the system prompt so the
+    guidance is not paid for twice on every request.
     """
     search_objects_properties: dict[str, Any] = {
         "camera": {
@@ -270,26 +274,13 @@ def get_tool_definitions(
         },
         "label": {
             "type": "string",
-            "description": (
-                "Generic object class to filter by — one of the tracked detector "
-                "labels such as 'person', 'package', 'car', 'dog', 'bird'. Use "
-                "this for broad queries like 'show me all cars today'. Combine "
-                "with semantic_query when the user also describes appearance or "
-                "behavior (e.g. label='person', semantic_query='riding a lawn "
-                "mower')."
-            ),
+            "description": "Tracked object class to filter by.",
         },
         "sub_label": {
             "type": "string",
             "description": (
-                "Filter by a DISCRETE NAMED entity recognized in the detection. "
-                "Use this for: a known person's name ('John'), a delivery "
-                "company ('Amazon', 'UPS'), a recognized animal species or "
-                "breed ('blue jay', 'cardinal', 'golden retriever'), or a "
-                "license plate string. When filtering by a specific name, set "
-                "only sub_label and leave label unset. Do NOT use sub_label "
-                "for descriptions of appearance, clothing, or actions — those "
-                "belong in semantic_query."
+                "Name recognized in the detection: a person, delivery company, "
+                "animal species or breed, or license plate."
             ),
         },
         "after": {
@@ -313,20 +304,11 @@ def get_tool_definitions(
     }
 
     if attribute_classifications:
-        model_outline = "; ".join(
-            f"{m['name']} (applies to {', '.join(m['objects']) or 'any object'})"
-            for m in attribute_classifications
-        )
         search_objects_properties["attribute"] = {
             "type": "string",
             "description": (
-                "Filter by a classification attribute label produced by a "
-                "configured attribute classification model. Use this INSTEAD "
-                "of semantic_query when the user's request matches one of "
-                "these classifications. Configured models: "
-                f"{model_outline}. "
-                "Set the value to the attribute label that matches the user's "
-                "phrasing (case-sensitive)."
+                "Attribute label produced by a configured classification model "
+                "(case-sensitive)."
             ),
         }
 
@@ -334,29 +316,12 @@ def get_tool_definitions(
         search_objects_properties["semantic_query"] = {
             "type": "string",
             "description": (
-                "Optional natural-language description of a PHYSICAL "
-                "CHARACTERISTIC, APPEARANCE, or ACTIVITY the user mentioned, "
-                "used to semantically narrow results. Only set this when the "
-                "user describes something beyond what label and sub_label can "
-                "express on their own.\n"
-                "USE for descriptive phrases like: 'riding a lawn mower', "
-                "'wearing a red jacket', 'carrying a package', 'walking a "
-                "dog', 'on a bicycle', 'holding an umbrella'.\n"
-                "DO NOT USE for:\n"
-                "- specific named people, pets, or delivery companies → use sub_label\n"
-                "- animal species or breed names like 'blue jay', 'cardinal', "
-                "'golden retriever' → use sub_label\n"
-                "- license plate strings → use sub_label\n"
-                "- generic object queries like 'all cars today' or 'every "
-                "person' → use label alone with no semantic_query\n"
-                "When set, combine with label/time/camera/zone filters as "
-                "usual (e.g. label='person', semantic_query='riding a lawn "
-                "mower', after='2024-05-01T00:00:00Z')."
+                "Description of an appearance or activity, used to semantically "
+                "narrow results."
                 + (
-                    " The configured embeddings model only understands "
-                    "English, so always write semantic_query in English, "
-                    "translating the user's description if they phrased it "
-                    "in another language."
+                    " The configured embeddings model only understands English, so "
+                    "always write this in English, translating the user's "
+                    "description if they phrased it in another language."
                     if embeddings_language == "english"
                     else ""
                 )
@@ -364,26 +329,10 @@ def get_tool_definitions(
         }
 
     search_objects_description = (
-        "Search the historical record of detected objects in Frigate. "
-        "Use this ONLY for questions about the PAST — e.g. 'did anyone come by today?', "
-        "'when was the last car?', 'show me detections from yesterday'. "
-        "Do NOT use this for monitoring or alerting requests about future events — "
-        "use start_camera_watch instead for those. "
-        "An 'object' in Frigate represents a tracked detection (e.g., a person, package, car).\n\n"
-        "Choose filters based on what the user is asking for:\n"
-        "- Generic class query ('show me all cars today'): set `label` only.\n"
-        "- Specific NAMED entity (known person, delivery company, animal "
-        "species/breed like 'blue jay' or 'golden retriever', license "
-        "plate): set `sub_label` only and leave `label` unset.\n"
+        "Search the historical record of tracked detections. Use this ONLY for "
+        "questions about the PAST, e.g. 'did anyone come by today?', 'when was the "
+        "last car?'. For alerting on future events use start_camera_watch instead."
     )
-    if semantic_search_enabled:
-        search_objects_description += (
-            "- Physical CHARACTERISTIC, APPEARANCE, or ACTIVITY that is not a "
-            "discrete name ('person riding a lawn mower', 'someone in a red "
-            "jacket', 'person carrying a package'): set `semantic_query` with "
-            "the descriptive phrase, optionally alongside `label` for the "
-            "object class. Do NOT put descriptive phrases in sub_label."
-        )
 
     return [
         {
@@ -398,20 +347,30 @@ def get_tool_definitions(
                 "required": [],
             },
         },
+        {
+            "type": "function",
+            "function": {
+                "name": "get_categorized_object_names",
+                "description": (
+                    "Every name that can be attached as a sub_label, grouped by object "
+                    "type: recognized faces, named license plates, classification "
+                    "categories, and delivery logos. Takes no arguments and always "
+                    "returns the complete map."
+                ),
+                "parameters": {
+                    "type": "object",
+                    "properties": {},
+                    "required": [],
+                },
+            },
+        },
         {
             "type": "function",
             "function": {
                 "name": "find_similar_objects",
                 "description": (
-                    "Find tracked objects that are visually and semantically similar "
-                    "to a specific past event. Use this when the user references a "
-                    "particular object they have seen and wants to find other "
-                    "sightings of the same or similar one ('that green car', 'the "
-                    "person in the red jacket', 'the package that was delivered'). "
-                    "Prefer this over search_objects whenever the user's intent is "
-                    "'find more like this specific one.' Use search_objects first "
-                    "only if you need to locate the anchor event. Requires semantic "
-                    "search to be enabled."
+                    "Find tracked objects visually and semantically similar to a "
+                    "specific past event. Requires semantic search to be enabled."
                 ),
                 "parameters": {
                     "type": "object",
@@ -473,9 +432,8 @@ def get_tool_definitions(
             "function": {
                 "name": "set_camera_state",
                 "description": (
-                    "Change a camera's feature state (e.g., turn detection on/off, enable/disable recordings). "
-                    "Use camera='*' to apply to all cameras at once. "
-                    "Only call this tool when the user explicitly asks to change a camera setting. "
+                    "Change a camera's feature state, e.g. turn detection on or off. "
+                    "Only call this when the user explicitly asks to change a setting. "
                     "Requires admin privileges."
                 ),
                 "parameters": {
@@ -495,7 +453,7 @@ def get_tool_definitions(
                                 "motion",
                                 "enabled",
                                 "birdseye",
-                                "birdseye_mode",
+                                "birdseye_modes",
                                 "improve_contrast",
                                 "ptz_autotracker",
                                 "motion_contour_area",
@@ -510,14 +468,14 @@ def get_tool_definitions(
                             ],
                             "description": (
                                 "The feature to change. Most features accept ON or OFF. "
-                                "birdseye_mode accepts CONTINUOUS, MOTION, or OBJECTS. "
+                                "birdseye_modes accepts CONTINUOUS, MOTION, ALL_OBJECTS, ALERTS, DETECTIONS, NONE, or a comma-separated combination. "
                                 "motion_contour_area and motion_threshold accept a number. "
                                 "profile accepts a profile name or 'none' to deactivate (requires camera='*')."
                             ),
                         },
                         "value": {
                             "type": "string",
-                            "description": "The value to set. ON or OFF for toggles, a number for thresholds, a profile name or 'none' for profile.",
+                            "description": "The value to set, as accepted by the chosen feature.",
                         },
                     },
                     "required": ["camera", "feature", "value"],
@@ -529,11 +487,9 @@ def get_tool_definitions(
             "function": {
                 "name": "get_live_context",
                 "description": (
-                    "Get the current live image and detection information for a single camera: objects being tracked, "
-                    "zones, timestamps. Use this to understand what is visible in the live view. "
-                    "Call this when answering questions about what is happening right now on a specific camera. "
-                    "Operates on one camera at a time; call the tool again for each additional camera. "
-                    "Wildcards and empty values are not accepted."
+                    "Current live image and detections (tracked objects, zones, "
+                    "timestamps) for one camera. Use this for questions about what is "
+                    "happening right now. Call it again for each additional camera."
                 ),
                 "parameters": {
                     "type": "object",
@@ -541,8 +497,8 @@ def get_tool_definitions(
                         "camera": {
                             "type": "string",
                             "description": (
-                                "Exact name of a single camera to get live context for. "
-                                "Wildcards (e.g. '*', 'all') and empty strings are not accepted."
+                                "Exact name of a single camera. Wildcards (e.g. '*', "
+                                "'all') and empty strings are not accepted."
                             ),
                         },
                     },
@@ -555,10 +511,9 @@ def get_tool_definitions(
             "function": {
                 "name": "start_camera_watch",
                 "description": (
-                    "Start a continuous VLM watch job that monitors a camera and sends a notification "
-                    "when a specified condition is met. Use this when the user wants to be alerted about "
-                    "a future event, e.g. 'tell me when guests arrive' or 'notify me when the package is picked up'. "
-                    "Only one watch job can run at a time. Returns a job ID."
+                    "Start a continuous watch job that monitors a camera and notifies "
+                    "the user when a condition is met, e.g. 'tell me when guests "
+                    "arrive'. Only one watch job can run at a time. Returns a job ID."
                 ),
                 "parameters": {
                     "type": "object",
@@ -598,10 +553,7 @@ def get_tool_definitions(
             "type": "function",
             "function": {
                 "name": "stop_camera_watch",
-                "description": (
-                    "Cancel the currently running VLM watch job. Use this when the user wants to "
-                    "stop a previously started watch, e.g. 'stop watching the front door'."
-                ),
+                "description": "Cancel the currently running watch job.",
                 "parameters": {
                     "type": "object",
                     "properties": {},
@@ -614,11 +566,9 @@ def get_tool_definitions(
             "function": {
                 "name": "get_profile_status",
                 "description": (
-                    "Get the current profile status including the active profile and "
-                    "timestamps of when each profile was last activated. Use this to "
-                    "determine time periods for recap requests — e.g. when the user asks "
-                    "'what happened while I was away?', call this first to find the relevant "
-                    "time window based on profile activation history."
+                    "Get the active profile and when each profile was last activated. "
+                    "Call this before get_recap to derive the time window for requests "
+                    "like 'what happened while I was away?'."
                 ),
                 "parameters": {
                     "type": "object",
@@ -632,11 +582,9 @@ def get_tool_definitions(
             "function": {
                 "name": "get_recap",
                 "description": (
-                    "Get a recap of all activity (alerts and detections) for a given time period. "
-                    "Use this after calling get_profile_status to retrieve what happened during "
-                    "a specific window — e.g. 'what happened while I was away?'. Returns a "
-                    "chronological list of activity with camera, objects, zones, and GenAI-generated "
-                    "descriptions when available. Summarize the results for the user."
+                    "Get all activity (alerts and detections) for a time period, as a "
+                    "chronological list with camera, objects, zones, and descriptions "
+                    "when available. Summarize the results for the user."
                 ),
                 "parameters": {
                     "type": "object",
@@ -723,14 +671,13 @@ def build_chat_system_prompt(
         )
         speed_units_section = f"\n\nReport object speeds to the user in {speed_unit}."
 
-    semantic_search_section = ""
+    filter_routing_section = (
+        "\n\nWhen routing a search_objects call, pick filters by the shape of the user's request:\n"
+        "- Generic class ('show me all cars today'): set `label` only.\n"
+        "- Specific named entity — a known person ('John'), delivery company ('Amazon'), animal species/breed ('blue jay', 'golden retriever'), or license plate: set `sub_label` only and leave `label` unset. Call get_categorized_object_names first and use the exact spelling it returns; a guessed spelling matches nothing. If the name is absent, say it is not configured rather than searching for it."
+    )
     if semantic_search_enabled:
-        semantic_search_section = (
-            "\n\nWhen routing a search_objects call, pick filters by the shape of the user's request:\n"
-            "- Generic class ('show me all cars today'): set `label` only.\n"
-            "- Specific named entity — a known person ('John'), delivery company ('Amazon'), animal species/breed ('blue jay', 'cardinal', 'golden retriever'), or license plate: set `sub_label` only and leave `label` unset.\n"
-            "- Physical characteristic, appearance, or activity that is NOT a discrete name ('find me people riding a lawn mower', 'someone in a red jacket', 'a person carrying a package'): set `semantic_query` with the descriptive phrase, optionally combined with `label` for the object class. Never put descriptive phrases in `sub_label`."
-        )
+        filter_routing_section += "\n- Physical characteristic, appearance, or activity that is NOT a discrete name ('riding a lawn mower', 'someone in a red jacket'): set `semantic_query` with the descriptive phrase, optionally combined with `label`. Never put descriptive phrases in `sub_label`."
 
     attribute_classification_section = ""
     if attribute_classifications:
@@ -739,9 +686,9 @@ def build_chat_system_prompt(
             for m in attribute_classifications
         )
         attribute_classification_section = (
-            "\n\nAttribute classification models are configured for the following object types:\n"
+            "\n\nConfigured attribute classification models:\n"
             f"{model_lines}\n"
-            "When the user's request matches one of these classifications, set the search_objects `attribute` field to the matching label rather than using `semantic_query`. Reserve `semantic_query` for descriptive phrases that fall outside the configured attribute labels."
+            "When the user's request matches one of these classifications, set the search_objects `attribute` field to the matching label (case-sensitive) rather than using `semantic_query`. Reserve `semantic_query` for descriptive phrases outside the configured attribute labels."
         )
 
     return f"""You are a helpful assistant for Frigate, a security camera NVR system. You help users answer questions about their cameras, detected objects, and events.
@@ -750,9 +697,6 @@ Current server local date and time: {current_date_str} at {current_time_str}
 
 Do not start your response with phrases like "I will check...", "Let me see...", or "Let me look...". Answer directly.
 
-Always present times to the user in the server's local timezone. When tool results include start_time_local and end_time_local, use those exact strings when listing or describing detection times—do not convert or invent timestamps. Do not use UTC or ISO format with Z for the user-facing answer unless the tool result only provides Unix timestamps without local time fields.
-When users ask about "today", "yesterday", "this week", etc., use the current date above as reference.
-When searching for objects or events, use ISO 8601 format for dates (e.g., {current_date_str}T00:00:00Z for the start of today).
-Always be accurate with time calculations based on the current date provided.
+Always present times in the server's local timezone. When tool results include start_time_local and end_time_local, quote those strings exactly; never convert or invent timestamps, and fall back to UTC or ISO format only when a result has no local time fields. Resolve relative dates like "today" or "this week" against the current date above, and pass dates to tools in ISO 8601 (e.g. {current_date_str}T00:00:00Z for the start of today).
 
-When a user refers to a specific object they have seen or describe with identifying details ("that green car", "the person in the red jacket", "a package left today"), prefer the find_similar_objects tool over search_objects. Use search_objects first only to locate the anchor event, then pass its id to find_similar_objects. For generic queries like "show me all cars today", keep using search_objects. If a user message begins with [attached_event:<id>], treat that event id as the anchor for any similarity or "tell me more" request in the same message and call find_similar_objects with that id.{semantic_search_section}{attribute_classification_section}{cameras_section}{speed_units_section}"""
+When the user refers to a specific object they have seen ("that green car", "the person in the red jacket", "a package left today"), prefer find_similar_objects over search_objects, using search_objects only to locate the anchor event and passing its id along. Keep search_objects for generic queries like "show me all cars today". If a user message begins with [attached_event:<id>], treat that id as the anchor for any similarity or "tell me more" request in the same message.{filter_routing_section}{attribute_classification_section}{cameras_section}{speed_units_section}"""

frigate/jobs/motion_search.py

@@ -15,7 +15,7 @@ import numpy as np
 
 from frigate.comms.inter_process import InterProcessRequestor
 from frigate.config import FrigateConfig
-from frigate.const import UPDATE_JOB_STATE
+from frigate.const import STREAM_TYPE_MAIN, UPDATE_JOB_STATE
 from frigate.jobs.job import Job
 from frigate.jobs.manager import (
     get_job_by_id,
@@ -485,6 +485,7 @@ class MotionSearchRunner(threading.Thread):
                 )
             )
             .where(Recordings.camera == camera_name)
+            .where(Recordings.stream_type == STREAM_TYPE_MAIN)
             .order_by(Recordings.start_time.asc())
         )
 

frigate/jobs/motion_search_decode.py

@@ -17,6 +17,7 @@ import numpy as np
 
 from frigate.config import CameraConfig
 from frigate.ffmpeg_presets import parse_preset_hardware_acceleration_decode
+from frigate.util.ffmpeg import terminate_ffmpeg_stream
 from frigate.util.services import auto_detect_hwaccel
 
 logger = logging.getLogger(__name__)
@@ -88,25 +89,6 @@ def _read_exact(stream: IO[bytes], size: int) -> bytes | None:
     return bytes(buf)
 
 
-def _terminate(proc: sp.Popen[bytes]) -> None:
-    """Stop an ffmpeg decode process promptly."""
-    # Close the read end first so a blocked ffmpeg write unblocks (ffmpeg then
-    # sees a broken pipe), then signal it. The resulting ffmpeg write error is
-    # harmless and goes to the captured stderr.
-    if proc.stdout is not None:
-        try:
-            proc.stdout.close()
-        except OSError:
-            pass
-    if proc.poll() is None:
-        proc.terminate()
-        try:
-            proc.wait(timeout=5)
-        except sp.TimeoutExpired:
-            proc.kill()
-            proc.wait()
-
-
 KEYFRAME_MAX_GAP_SECONDS = 2.0
 
 
@@ -222,7 +204,7 @@ def _run_vod_decode(
             count += 1
             yield frame
     finally:
-        _terminate(proc)
+        terminate_ffmpeg_stream(proc)
         stderr_file.close()
 
     if count == 0 and software_retry and not should_stop():

frigate/models.py

@@ -79,6 +79,12 @@ class Recordings(Model):
     segment_size = FloatField(default=0)  # this should be stored as MB
     regions = IntegerField(null=True)
     motion_heatmap = JSONField(null=True)  # 16x16 grid, 256 values (0-255)
+    keyframes = JSONField(null=True)  # ms offsets; NULL = unprobed (legacy rows)
+    stream_type = CharField(default="main", max_length=8)
+    has_audio = BooleanField(null=True)  # NULL = unknown (legacy rows)
+    audio_rate = IntegerField(null=True)  # Hz; NULL = unknown (legacy rows)
+    audio_codec = CharField(null=True, max_length=20)  # NULL = unknown (legacy rows)
+    video_codec = CharField(null=True, max_length=20)  # NULL = unknown (legacy rows)
 
 
 class ExportCase(Model):

frigate/object_detection/util.py

@@ -5,7 +5,19 @@ import threading
 
 from numpy import ndarray
 
-from frigate.detectors.detector_config import InputTensorEnum
+from frigate.detectors.detector_config import InputTensorEnum, ModelConfig
+
+
+def detection_frame_size(model: ModelConfig) -> int:
+    """Get the shared memory size a camera needs to hand frames to a model.
+
+    Args:
+        model: The model the camera runs on
+
+    Returns:
+        Size in bytes of one model input frame
+    """
+    return model.height * model.width * 3
 
 
 class RequestStore:

frigate/output/birdseye.py

@@ -9,6 +9,7 @@ import queue
 import subprocess as sp
 import threading
 import traceback
+from dataclasses import dataclass
 from multiprocessing.synchronize import Event as MpEvent
 from typing import Any
 
@@ -16,9 +17,11 @@ import cv2
 import numpy as np
 
 from frigate.comms.inter_process import InterProcessRequestor
+from frigate.comms.review_updater import ReviewDataSubscriber
 from frigate.config import BirdseyeModeEnum, FfmpegConfig, FrigateConfig
 from frigate.const import BASE_DIR, BIRDSEYE_PIPE, INSTALL_DIR, UPDATE_BIRDSEYE_LAYOUT
 from frigate.output.ws_auth import ws_has_camera_access
+from frigate.review.types import SeverityEnum
 from frigate.util.image import (
     SharedMemoryFrameManager,
     copy_yuv_to_position,
@@ -28,6 +31,15 @@ from frigate.util.image import (
 logger = logging.getLogger(__name__)
 
 
+@dataclass(frozen=True, slots=True)
+class BirdseyeActivity:
+    """Activity signals used to decide whether a camera is shown in Birdseye."""
+
+    has_object: bool
+    has_motion: bool
+    severity: str | None
+
+
 def get_standard_aspect_ratio(width: int, height: int) -> tuple[int, int]:
     """Ensure that only standard aspect ratios are used."""
     # it is important that all ratios have the same scale
@@ -357,6 +369,7 @@ class BirdsEyeFrameManager:
                 settings.detect.height,
             ],
             "last_active_frame": 0.0,
+            "live_active": False,
             "current_frame": 0.0,
             "layout_frame": 0.0,
             "channel_dims": {
@@ -408,19 +421,33 @@ class BirdsEyeFrameManager:
             channel_dims,
         )
 
-    def camera_active(
-        self, mode: Any, object_box_count: int, motion_box_count: int
+    def camera_threshold_active(
+        self,
+        modes: list[BirdseyeModeEnum],
+        activity: BirdseyeActivity,
     ) -> bool:
-        if mode == BirdseyeModeEnum.continuous:
-            return True
+        """Return whether activity subject to inactivity_threshold is present."""
+        return (BirdseyeModeEnum.motion in modes and activity.has_motion) or (
+            BirdseyeModeEnum.all_objects in modes and activity.has_object
+        )
 
-        if mode == BirdseyeModeEnum.motion and motion_box_count > 0:
-            return True
-
-        if mode == BirdseyeModeEnum.objects and object_box_count > 0:
-            return True
-
-        return False
+    def camera_live_active(
+        self,
+        modes: list[BirdseyeModeEnum],
+        activity: BirdseyeActivity,
+    ) -> bool:
+        """Return whether activity that ends the moment it stops is present."""
+        return (
+            BirdseyeModeEnum.continuous in modes
+            or (
+                BirdseyeModeEnum.alerts in modes
+                and activity.severity == SeverityEnum.alert
+            )
+            or (
+                BirdseyeModeEnum.detections in modes
+                and activity.severity == SeverityEnum.detection
+            )
+        )
 
     def get_camera_coordinates(self) -> dict[str, dict[str, int]]:
         """Return the coordinates of each camera in the current layout."""
@@ -451,9 +478,15 @@ class BirdsEyeFrameManager:
                 and self.config.cameras[cam].birdseye.enabled
                 and self.config.cameras[cam].enabled_in_config
                 and self.config.cameras[cam].enabled
-                and cam_data["last_active_frame"] > 0
-                and cam_data["current_frame_time"] - cam_data["last_active_frame"]
-                < self.config.birdseye.inactivity_threshold
+                and (
+                    cam_data["live_active"]
+                    or (
+                        cam_data["last_active_frame"] > 0
+                        and cam_data["current_frame_time"]
+                        - cam_data["last_active_frame"]
+                        < self.config.birdseye.inactivity_threshold
+                    )
+                )
             ]
         )
         logger.debug(f"Active cameras: {active_cameras}")
@@ -470,7 +503,9 @@ class BirdsEyeFrameManager:
                 limited_active_cameras = sorted(
                     active_cameras,
                     key=lambda active_camera: (
-                        self.cameras[active_camera]["current_frame_time"]
+                        0.0
+                        if self.cameras[active_camera]["live_active"]
+                        else self.cameras[active_camera]["current_frame_time"]
                         - self.cameras[active_camera]["last_active_frame"]
                     ),
                 )
@@ -604,112 +639,92 @@ class BirdsEyeFrameManager:
     ) -> list[list[Any]] | None:
         """Calculate the optimal layout for 2+ cameras."""
 
-        def map_layout(
-            camera_layout: list[list[Any]], row_height: int
-        ) -> tuple[int, int, list[list[Any]] | None]:
-            """Map the calculated layout."""
-            candidate_layout = []
-            starting_x = 0
-            x = 0
-            max_width = 0
-            y = 0
+        def find_available_x(
+            current_x: int,
+            width: int,
+            reserved_ranges: list[tuple[int, int]],
+            max_width: int,
+        ) -> int | None:
+            """Find the first horizontal slot that does not collide with reservations."""
+            x = current_x
 
-            for row in camera_layout:
-                final_row = []
-                max_width = max(max_width, x)
-                x = starting_x
-                for cameras in row:
-                    camera_dims = self.cameras[cameras[0]]["dimensions"].copy()
-                    camera_aspect = cameras[1]
+            for reserved_start, reserved_end in sorted(reserved_ranges):
+                if x >= reserved_end:
+                    continue
 
-                    if camera_dims[1] > camera_dims[0]:
-                        scaled_height = int(row_height * 2)
-                        scaled_width = int(scaled_height * camera_aspect)
-                        starting_x = scaled_width
-                    else:
-                        scaled_height = row_height
-                        scaled_width = int(scaled_height * camera_aspect)
+                if x + width <= reserved_start:
+                    return x
 
-                    # layout is too large
-                    if (
-                        x + scaled_width > self.canvas.width
-                        or y + scaled_height > self.canvas.height
-                    ):
-                        return x + scaled_width, y + scaled_height, None
+                x = max(x, reserved_end)
 
-                    final_row.append((cameras[0], (x, y, scaled_width, scaled_height)))
-                    x += scaled_width
+            if x + width <= max_width:
+                return x
 
-                y += row_height
-                candidate_layout.append(final_row)
-
-            if max_width == 0:
-                max_width = x
-
-            return max_width, y, candidate_layout
-
-        canvas_aspect_x, canvas_aspect_y = self.canvas.get_aspect(coefficient)
-        camera_layout: list[list[Any]] = []
-        camera_layout.append([])
-        starting_x = 0
-        x = starting_x
-        y = 0
-        y_i = 0
-        max_y = 0
-        for camera in cameras_to_add:
-            camera_dims = self.cameras[camera]["dimensions"].copy()
-            camera_aspect_x, camera_aspect_y = self.canvas.get_camera_aspect(
-                camera, camera_dims[0], camera_dims[1]
-            )
-
-            if camera_dims[1] > camera_dims[0]:
-                portrait = True
-            else:
-                portrait = False
-
-            if (x + camera_aspect_x) <= canvas_aspect_x:
-                # insert if camera can fit on current row
-                camera_layout[y_i].append(
-                    (
-                        camera,
-                        camera_aspect_x / camera_aspect_y,
-                    )
-                )
-
-                if portrait:
-                    starting_x = camera_aspect_x
-                else:
-                    max_y = max(
-                        max_y,
-                        camera_aspect_y,
-                    )
-
-                x += camera_aspect_x
-            else:
-                # move on to the next row and insert
-                y += max_y
-                y_i += 1
-                camera_layout.append([])
-                x = starting_x
-
-                if x + camera_aspect_x > canvas_aspect_x:
-                    return None
-
-                camera_layout[y_i].append(
-                    (
-                        camera,
-                        camera_aspect_x / camera_aspect_y,
-                    )
-                )
-                x += camera_aspect_x
-
-        if y + max_y > canvas_aspect_y:
             return None
 
-        row_height = int(self.canvas.height / coefficient)
-        total_width, total_height, standard_candidate_layout = map_layout(
-            camera_layout, row_height
-        )
+        def map_layout(row_height: int) -> tuple[int, int, list[list[Any]] | None]:
+            """Lay out cameras row by row while reserving portrait spans for the next row."""
+            candidate_layout: list[list[Any]] = []
+            reserved_ranges: dict[int, list[tuple[int, int]]] = {}
+            current_row: list[Any] = []
+            row_index = 0
+            row_y = 0
+            row_x = 0
+            max_width = 0
+            max_height = 0
+
+            for camera in cameras_to_add:
+                camera_dims = self.cameras[camera]["dimensions"].copy()
+                camera_aspect_x, camera_aspect_y = self.canvas.get_camera_aspect(
+                    camera, camera_dims[0], camera_dims[1]
+                )
+                portrait = camera_dims[1] > camera_dims[0]
+                scaled_height = row_height * 2 if portrait else row_height
+                scaled_width = int(scaled_height * (camera_aspect_x / camera_aspect_y))
+
+                while True:
+                    x = find_available_x(
+                        row_x,
+                        scaled_width,
+                        reserved_ranges.get(row_index, []),
+                        self.canvas.width,
+                    )
+
+                    if x is not None and row_y + scaled_height <= self.canvas.height:
+                        current_row.append(
+                            (camera, (x, row_y, scaled_width, scaled_height))
+                        )
+                        row_x = x + scaled_width
+                        max_width = max(max_width, row_x)
+                        max_height = max(max_height, row_y + scaled_height)
+
+                        if portrait:
+                            reserved_ranges.setdefault(row_index + 1, []).append(
+                                (x, row_x)
+                            )
+
+                        break
+
+                    if current_row:
+                        candidate_layout.append(current_row)
+                        current_row = []
+
+                    row_index += 1
+                    row_y = row_index * row_height
+                    row_x = 0
+
+                    if row_y + scaled_height > self.canvas.height:
+                        overflow_width = max(max_width, scaled_width)
+                        overflow_height = row_y + scaled_height
+                        return overflow_width, overflow_height, None
+
+            if current_row:
+                candidate_layout.append(current_row)
+
+            return max_width, max_height, candidate_layout
+
+        row_height = max(1, int(self.canvas.height / coefficient))
+        total_width, total_height, standard_candidate_layout = map_layout(row_height)
 
         if not standard_candidate_layout:
             # if standard layout didn't work
@@ -718,9 +733,9 @@ class BirdsEyeFrameManager:
                 total_width / self.canvas.width,
                 total_height / self.canvas.height,
             )
-            row_height = int(row_height / scale_down_percent)
+            row_height = max(1, int(row_height / scale_down_percent))
             total_width, total_height, standard_candidate_layout = map_layout(
-                camera_layout, row_height
+                row_height
             )
 
             if not standard_candidate_layout:
@@ -734,8 +749,8 @@ class BirdsEyeFrameManager:
             1 / (total_width / self.canvas.width),
             1 / (total_height / self.canvas.height),
         )
-        row_height = int(row_height * scale_up_percent)
-        _, _, scaled_layout = map_layout(camera_layout, row_height)
+        row_height = max(1, int(row_height * scale_up_percent))
+        _, _, scaled_layout = map_layout(row_height)
 
         if scaled_layout:
             return scaled_layout
@@ -745,8 +760,7 @@ class BirdsEyeFrameManager:
     def update(
         self,
         camera: str,
-        object_count: int,
-        motion_count: int,
+        activity: BirdseyeActivity,
         frame_time: float,
         frame: np.ndarray,
     ) -> tuple[bool, bool]:
@@ -760,22 +774,30 @@ class BirdsEyeFrameManager:
             return False, False
 
         force_update = False
+        camera_state = self.cameras.get(camera)
+
+        if camera_state is None:
+            return False, False
 
         # disabling birdseye is a little tricky
         if not camera_config.birdseye.enabled or not camera_config.enabled:
-            # if we've rendered a frame (we have a value for last_active_frame)
-            # then we need to set it to zero
-            if self.cameras[camera]["last_active_frame"] > 0:
-                self.cameras[camera]["last_active_frame"] = 0
+            # if we've rendered a frame (we have activity state) then clear it
+            if camera_state["last_active_frame"] > 0 or camera_state["live_active"]:
+                camera_state["last_active_frame"] = 0
+                camera_state["live_active"] = False
                 force_update = True
             else:
                 return False, False
 
         # update the last active frame for the camera
-        self.cameras[camera]["current_frame"] = frame.copy()
-        self.cameras[camera]["current_frame_time"] = frame_time
-        if self.camera_active(camera_config.birdseye.mode, object_count, motion_count):
-            self.cameras[camera]["last_active_frame"] = frame_time
+        camera_state["current_frame"] = frame.copy()
+        camera_state["current_frame_time"] = frame_time
+        modes = camera_config.birdseye.modes
+
+        if self.camera_threshold_active(modes, activity):
+            camera_state["last_active_frame"] = frame_time
+
+        camera_state["live_active"] = self.camera_live_active(modes, activity)
 
         now = datetime.datetime.now().timestamp()
 
@@ -834,6 +856,8 @@ class Birdseye:
         self.frame_manager = SharedMemoryFrameManager()
         self.stop_event = stop_event
         self.requestor = InterProcessRequestor()
+        self.review_subscriber = ReviewDataSubscriber("")
+        self.review_severity: dict[str, str] = {}
         self.idle_fps: float = self.config.birdseye.idle_heartbeat_fps
         self._idle_interval: float | None = (
             (1.0 / self.idle_fps) if self.idle_fps > 0 else None
@@ -864,6 +888,21 @@ class Birdseye:
         self.birdseye_manager.clear_frame()
         self.__send_new_frame()
 
+    def check_review_updates(self) -> None:
+        """Drain review updates so each camera's active severity stays current."""
+        while True:
+            update = self.review_subscriber.check_for_update(timeout=0)
+
+            if update is None:
+                break
+
+            camera = update["after"]["camera"]
+
+            if update["type"] == "end":
+                self.review_severity.pop(camera, None)
+            else:
+                self.review_severity[camera] = update["after"]["severity"]
+
     def add_camera(self, camera: str) -> None:
         """Add a camera to the birdseye manager."""
         self.birdseye_manager.add_camera(camera)
@@ -872,6 +911,7 @@ class Birdseye:
     def remove_camera(self, camera: str) -> None:
         """Remove a camera from the birdseye manager."""
         self.birdseye_manager.remove_camera(camera)
+        self.review_severity.pop(camera, None)
         logger.debug(f"Removed camera {camera} from birdseye")
 
     def write_data(
@@ -882,10 +922,18 @@ class Birdseye:
         frame_time: float,
         frame: np.ndarray,
     ) -> None:
+        activity = BirdseyeActivity(
+            has_object=any(
+                not tracked_object["false_positive"]
+                for tracked_object in current_tracked_objects
+            ),
+            has_motion=bool(motion_boxes),
+            severity=self.review_severity.get(camera),
+        )
+
         frame_changed, frame_layout_changed = self.birdseye_manager.update(
             camera,
-            len([o for o in current_tracked_objects if not o["stationary"]]),
-            len(motion_boxes),
+            activity,
             frame_time,
             frame,
         )
@@ -906,5 +954,6 @@ class Birdseye:
                 self.__send_new_frame()
 
     def stop(self) -> None:
+        self.review_subscriber.stop()
         self.converter.join()
         self.broadcaster.join()