Bump protobuf from 3.20.3 to 6.33.0 in /docker/tensorrt

Bumps [protobuf](https://github.com/protocolbuffers/protobuf) from 3.20.3 to 6.33.0.
- [Release notes](https://github.com/protocolbuffers/protobuf/releases)
- [Changelog](https://github.com/protocolbuffers/protobuf/blob/main/protobuf_release.bzl)
- [Commits](https://github.com/protocolbuffers/protobuf/commits)

---
updated-dependencies:
- dependency-name: protobuf
  dependency-version: 6.33.0
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>

.cursor/rules/frontend-always-use-translation-files.mdc

@@ -0,0 +1,6 @@
+---
+globs: ["**/*.ts", "**/*.tsx"]
+alwaysApply: false
+---
+
+Never write strings in the frontend directly, always write to and reference the relevant translations file.

docker/main/install_deps.sh

@@ -20,6 +20,7 @@ apt-get -qq install --no-install-recommends -y \
     libgl1 \
     libglib2.0-0 \
     libusb-1.0.0 \
+    python3-h2 \
     libgomp1  # memryx detector
 
 update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 1
@@ -95,6 +96,9 @@ if [[ "${TARGETARCH}" == "amd64" ]]; then
 
     apt-get -qq install -y ocl-icd-libopencl1
 
+    # install libtbb12 for NPU support
+    apt-get -qq install -y libtbb12
+
     rm -f /usr/share/keyrings/intel-graphics.gpg
     rm -f /etc/apt/sources.list.d/intel-gpu-jammy.list
 
@@ -115,6 +119,11 @@ if [[ "${TARGETARCH}" == "amd64" ]]; then
     wget https://github.com/intel/compute-runtime/releases/download/24.52.32224.5/intel-level-zero-gpu_1.6.32224.5_amd64.deb
     wget https://github.com/intel/intel-graphics-compiler/releases/download/v2.5.6/intel-igc-opencl-2_2.5.6+18417_amd64.deb
     wget https://github.com/intel/intel-graphics-compiler/releases/download/v2.5.6/intel-igc-core-2_2.5.6+18417_amd64.deb
+    # npu packages
+    wget https://github.com/oneapi-src/level-zero/releases/download/v1.21.9/level-zero_1.21.9+u22.04_amd64.deb
+    wget https://github.com/intel/linux-npu-driver/releases/download/v1.17.0/intel-driver-compiler-npu_1.17.0.20250508-14912879441_ubuntu22.04_amd64.deb
+    wget https://github.com/intel/linux-npu-driver/releases/download/v1.17.0/intel-fw-npu_1.17.0.20250508-14912879441_ubuntu22.04_amd64.deb
+    wget https://github.com/intel/linux-npu-driver/releases/download/v1.17.0/intel-level-zero-npu_1.17.0.20250508-14912879441_ubuntu22.04_amd64.deb
 
     dpkg -i *.deb
     rm *.deb

docker/main/requirements.txt

@@ -1,2 +1 @@
 scikit-build == 0.18.*
-nvidia-pyindex

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

@@ -73,6 +73,8 @@ http {
         vod_manifest_segment_durations_mode accurate;
         vod_ignore_edit_list on;
         vod_segment_duration 10000;
+
+        # MPEG-TS settings (not used when fMP4 is enabled, kept for reference)
         vod_hls_mpegts_align_frames off;
         vod_hls_mpegts_interleave_frames on;
 
@@ -105,6 +107,10 @@ http {
             aio threads;
             vod hls;
 
+            # Use fMP4 (fragmented MP4) instead of MPEG-TS for better performance
+            # Smaller segments, faster generation, better browser compatibility
+            vod_hls_container_format fmp4;
+
             secure_token $args;
             secure_token_types application/vnd.apple.mpegurl;
 
@@ -274,6 +280,18 @@ http {
                 include proxy.conf;
             }
 
+            # Allow unauthenticated access to the first_time_login endpoint
+            # so the login page can load help text before authentication.
+            location /api/auth/first_time_login {
+                auth_request off;
+                limit_except GET {
+                    deny all;
+                }
+                rewrite ^/api(/.*)$ $1 break;
+                proxy_pass http://frigate_api;
+                include proxy.conf;
+            }
+
             location /api/stats {
                 include auth_request.conf;
                 access_log off;

docker/tensorrt/requirements-amd64.txt

@@ -13,7 +13,6 @@ nvidia_cusolver_cu12==11.6.3.*; platform_machine == 'x86_64'
 nvidia_cusparse_cu12==12.5.1.*; platform_machine == 'x86_64'
 nvidia_nccl_cu12==2.23.4; platform_machine == 'x86_64'
 nvidia_nvjitlink_cu12==12.5.82; platform_machine == 'x86_64'
-tensorflow==2.19.*; platform_machine == 'x86_64'
 onnx==1.16.*; platform_machine == 'x86_64'
 onnxruntime-gpu==1.22.*; platform_machine == 'x86_64'
-protobuf==3.20.3; platform_machine == 'x86_64'
+protobuf==6.33.0; platform_machine == 'x86_64'

docker/tensorrt/requirements-models-arm64.txt

@@ -1,2 +1,2 @@
 onnx == 1.14.0; platform_machine == 'aarch64'
-protobuf == 3.20.3; platform_machine == 'aarch64'
+protobuf == 6.33.0; platform_machine == 'aarch64'

docs/docs/configuration/camera_specific.md

@@ -164,13 +164,35 @@ According to [this discussion](https://github.com/blakeblackshear/frigate/issues
 Cameras connected via a Reolink NVR can be connected with the http stream, use `channel[0..15]` in the stream url for the additional channels.
 The setup of main stream can be also done via RTSP, but isn't always reliable on all hardware versions. The example configuration is working with the oldest HW version RLN16-410 device with multiple types of cameras.
 
+<details>
+  <summary>Example Config</summary>
+
+:::tip
+
+Reolink's latest cameras support two way audio via go2rtc and other applications. It is important that the http-flv stream is still used for stability, a secondary rtsp stream can be added that will be using for the two way audio only.
+
+NOTE: The RTSP stream can not be prefixed with `ffmpeg:`, as go2rtc needs to handle the stream to support two way audio.
+
+Ensure HTTP is enabled in the camera's advanced network settings. To use two way talk with Frigate, see the [Live view documentation](/configuration/live#two-way-talk).
+
+:::
+
 ```yaml
 go2rtc:
   streams:
+    # example for connecting to a standard Reolink camera
     your_reolink_camera:
       - "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_main.bcs&user=username&password=password#video=copy#audio=copy#audio=opus"
     your_reolink_camera_sub:
       - "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_ext.bcs&user=username&password=password"
+    # example for connectin to a Reolink camera that supports two way talk
+    your_reolink_camera_twt:
+      - "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_main.bcs&user=username&password=password#video=copy#audio=copy#audio=opus"
+      - "rtsp://username:password@reolink_ip/Preview_01_sub
+    your_reolink_camera_twt_sub:
+      - "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_ext.bcs&user=username&password=password"
+      - "rtsp://username:password@reolink_ip/Preview_01_sub
+    # example for connecting to a Reolink NVR
     your_reolink_camera_via_nvr:
       - "ffmpeg:http://reolink_nvr_ip/flv?port=1935&app=bcs&stream=channel3_main.bcs&user=username&password=password" # channel numbers are 0-15
       - "ffmpeg:your_reolink_camera_via_nvr#audio=aac"
@@ -201,22 +223,7 @@ cameras:
           roles:
             - detect
 ```
-
-#### Reolink Doorbell
-
-The reolink doorbell supports two way audio via go2rtc and other applications. It is important that the http-flv stream is still used for stability, a secondary rtsp stream can be added that will be using for the two way audio only.
-
-Ensure HTTP is enabled in the camera's advanced network settings. To use two way talk with Frigate, see the [Live view documentation](/configuration/live#two-way-talk).
-
-```yaml
-go2rtc:
-  streams:
-    your_reolink_doorbell:
-      - "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_main.bcs&user=username&password=password#video=copy#audio=copy#audio=opus"
-      - rtsp://reolink_ip/Preview_01_sub
-    your_reolink_doorbell_sub:
-      - "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_ext.bcs&user=username&password=password"
-```
+</details>
 
 ### Unifi Protect Cameras
 

docs/docs/configuration/custom_classification/object_classification.md

@@ -10,9 +10,19 @@ Object classification allows you to train a custom MobileNetV2 classification mo
 Object classification models are lightweight and run very fast on CPU. Inference should be usable on virtually any machine that can run Frigate.
 
 Training the model does briefly use a high amount of system resources for about 1–3 minutes per training run. On lower-power devices, training may take longer.
-When running the `-tensorrt` image, Nvidia GPUs will automatically be used to accelerate training.
 
-### Sub label vs Attribute
+## Classes
+
+Classes are the categories your model will learn to distinguish between. Each class represents a distinct visual category that the model will predict.
+
+For object classification:
+
+- Define classes that represent different types or attributes of the detected object
+- Examples: For `person` objects, classes might be `delivery_person`, `resident`, `stranger`
+- Include a `none` class for objects that don't fit any specific category
+- Keep classes visually distinct to improve accuracy
+
+### Classification Type
 
 - **Sub label**:
 
@@ -67,7 +77,7 @@ When choosing which objects to classify, start with a small number of visually d
 ### Improving the Model
 
 - **Problem framing**: Keep classes visually distinct and relevant to the chosen object types.
-- **Data collection**: Use the model’s Train tab to gather balanced examples across times of day, weather, and distances.
+- **Data collection**: Use the model’s Recent Classification tab to gather balanced examples across times of day, weather, and distances.
 - **Preprocessing**: Ensure examples reflect object crops similar to Frigate’s boxes; keep the subject centered.
 - **Labels**: Keep label names short and consistent; include a `none` class if you plan to ignore uncertain predictions for sub labels.
 - **Threshold**: Tune `threshold` per model to reduce false assignments. Start at `0.8` and adjust based on validation.

docs/docs/configuration/custom_classification/state_classification.md

@@ -10,7 +10,17 @@ State classification allows you to train a custom MobileNetV2 classification mod
 State classification models are lightweight and run very fast on CPU. Inference should be usable on virtually any machine that can run Frigate.
 
 Training the model does briefly use a high amount of system resources for about 1–3 minutes per training run. On lower-power devices, training may take longer.
-When running the `-tensorrt` image, Nvidia GPUs will automatically be used to accelerate training.
+
+## Classes
+
+Classes are the different states an area on your camera can be in. Each class represents a distinct visual state that the model will learn to recognize.
+
+For state classification:
+
+- Define classes that represent mutually exclusive states
+- Examples: `open` and `closed` for a garage door, `on` and `off` for lights
+- Use at least 2 classes (typically binary states work best)
+- Keep class names clear and descriptive
 
 ## Example use cases
 
@@ -49,4 +59,4 @@ When choosing a portion of the camera frame for state classification, it is impo
 ### Improving the Model
 
 - **Problem framing**: Keep classes visually distinct and state-focused (e.g., `open`, `closed`, `unknown`). Avoid combining object identity with state in a single model unless necessary.
-- **Data collection**: Use the model’s Train tab to gather balanced examples across times of day and weather.
+- **Data collection**: Use the model’s Recent Classifications tab to gather balanced examples across times of day and weather.

docs/docs/configuration/face_recognition.md

@@ -70,7 +70,7 @@ Fine-tune face recognition with these optional parameters at the global level of
 - `min_faces`: Min face recognitions for the sub label to be applied to the person object.
   - Default: `1`
 - `save_attempts`: Number of images of recognized faces to save for training.
-  - Default: `100`.
+  - Default: `200`.
 - `blur_confidence_filter`: Enables a filter that calculates how blurry the face is and adjusts the confidence based on this.
   - Default: `True`.
 - `device`: Target a specific device to run the face recognition model on (multi-GPU installation).
@@ -114,9 +114,9 @@ When choosing images to include in the face training set it is recommended to al
 
 :::
 
-### Understanding the Train Tab
+### Understanding the Recent Recognitions Tab
 
-The Train tab in the face library displays recent face recognition attempts. Detected face images are grouped according to the person they were identified as potentially matching.
+The Recent Recognitions tab in the face library displays recent face recognition attempts. Detected face images are grouped according to the person they were identified as potentially matching.
 
 Each face image is labeled with a name (or `Unknown`) along with the confidence score of the recognition attempt. While each image can be used to train the system for a specific person, not all images are suitable for training.
 
@@ -140,7 +140,7 @@ Once front-facing images are performing well, start choosing slightly off-angle
 
 Start with the [Usage](#usage) section and re-read the [Model Requirements](#model-requirements) above.
 
-1. Ensure `person` is being _detected_. A `person` will automatically be scanned by Frigate for a face. Any detected faces will appear in the Train tab in the Frigate UI's Face Library.
+1. Ensure `person` is being _detected_. A `person` will automatically be scanned by Frigate for a face. Any detected faces will appear in the Recent Recognitions tab in the Frigate UI's Face Library.
 
    If you are using a Frigate+ or `face` detecting model:
 
@@ -161,6 +161,8 @@ Start with the [Usage](#usage) section and re-read the [Model Requirements](#mod
 
 Accuracy is definitely a going to be improved with higher quality cameras / streams. It is important to look at the DORI (Detection Observation Recognition Identification) range of your camera, if that specification is posted. This specification explains the distance from the camera that a person can be detected, observed, recognized, and identified. The identification range is the most relevant here, and the distance listed by the camera is the furthest that face recognition will realistically work.
 
+Some users have also noted that setting the stream in camera firmware to a constant bit rate (CBR) leads to better image clarity than with a variable bit rate (VBR).
+
 ### Why can't I bulk upload photos?
 
 It is important to methodically add photos to the library, bulk importing photos (especially from a general photo library) will lead to over-fitting in that particular scenario and hurt recognition performance.
@@ -186,7 +188,7 @@ Avoid training on images that already score highly, as this can lead to over-fit
 No, face recognition does not support negative training (i.e., explicitly telling it who someone is _not_). Instead, the best approach is to improve the training data by using a more diverse and representative set of images for each person.
 For more guidance, refer to the section above on improving recognition accuracy.
 
-### I see scores above the threshold in the train tab, but a sub label wasn't assigned?
+### I see scores above the threshold in the Recent Recognitions tab, but a sub label wasn't assigned?
 
 The Frigate considers the recognition scores across all recognition attempts for each person object. The scores are continually weighted based on the area of the face, and a sub label will only be assigned to person if a person is confidently recognized consistently. This avoids cases where a single high confidence recognition would throw off the results.
 

docs/docs/configuration/genai.md

@@ -17,18 +17,17 @@ To use Generative AI, you must define a single provider at the global level of y
 genai:
   provider: gemini
   api_key: "{FRIGATE_GEMINI_API_KEY}"
-  model: gemini-1.5-flash
+  model: gemini-2.0-flash
 
 cameras:
   front_camera:
-    objects:
     genai:
-        enabled: True # <- enable GenAI for your front camera
-        use_snapshot: True
-        objects:
-          - person
-        required_zones:
-          - steps
+      enabled: True # <- enable GenAI for your front camera
+      use_snapshot: True
+      objects:
+        - person
+      required_zones:
+        - steps
   indoor_camera:
     objects:
       genai:
@@ -80,7 +79,7 @@ Google Gemini has a free tier allowing [15 queries per minute](https://ai.google
 
 ### Supported Models
 
-You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://ai.google.dev/gemini-api/docs/models/gemini). At the time of writing, this includes `gemini-1.5-pro` and `gemini-1.5-flash`.
+You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://ai.google.dev/gemini-api/docs/models/gemini).
 
 ### Get API Key
 
@@ -97,7 +96,7 @@ To start using Gemini, you must first get an API key from [Google AI Studio](htt
 genai:
   provider: gemini
   api_key: "{FRIGATE_GEMINI_API_KEY}"
-  model: gemini-1.5-flash
+  model: gemini-2.0-flash
 ```
 
 :::note
@@ -112,7 +111,7 @@ OpenAI does not have a free tier for their API. With the release of gpt-4o, pric
 
 ### Supported Models
 
-You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://platform.openai.com/docs/models). At the time of writing, this includes `gpt-4o` and `gpt-4-turbo`.
+You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://platform.openai.com/docs/models).
 
 ### Get API Key
 
@@ -139,18 +138,19 @@ Microsoft offers several vision models through Azure OpenAI. A subscription is r
 
 ### Supported Models
 
-You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models). At the time of writing, this includes `gpt-4o` and `gpt-4-turbo`.
+You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models).
 
 ### Create Resource and Get API Key
 
-To start using Azure OpenAI, you must first [create a resource](https://learn.microsoft.com/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal#create-a-resource). You'll need your API key and resource URL, which must include the `api-version` parameter (see the example below). The model field is not required in your configuration as the model is part of the deployment name you chose when deploying the resource.
+To start using Azure OpenAI, you must first [create a resource](https://learn.microsoft.com/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal#create-a-resource). You'll need your API key, model name, and resource URL, which must include the `api-version` parameter (see the example below).
 
 ### Configuration
 
 ```yaml
 genai:
   provider: azure_openai
-  base_url: https://example-endpoint.openai.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2023-03-15-preview
+  base_url: https://instance.cognitiveservices.azure.com/openai/responses?api-version=2025-04-01-preview
+  model: gpt-5-mini
   api_key: "{FRIGATE_OPENAI_API_KEY}"
 ```
 
@@ -196,10 +196,10 @@ genai:
   model: llava
 
 objects:
-    prompt: "Analyze the {label} in these images from the {camera} security camera. Focus on the actions, behavior, and potential intent of the {label}, rather than just describing its appearance."
-    object_prompts:
-      person: "Examine the main person in these images. What are they doing and what might their actions suggest about their intent (e.g., approaching a door, leaving an area, standing still)? Do not describe the surroundings or static details."
-      car: "Observe the primary vehicle in these images. Focus on its movement, direction, or purpose (e.g., parking, approaching, circling). If it's a delivery vehicle, mention the company."
+  prompt: "Analyze the {label} in these images from the {camera} security camera. Focus on the actions, behavior, and potential intent of the {label}, rather than just describing its appearance."
+  object_prompts:
+    person: "Examine the main person in these images. What are they doing and what might their actions suggest about their intent (e.g., approaching a door, leaving an area, standing still)? Do not describe the surroundings or static details."
+    car: "Observe the primary vehicle in these images. Focus on its movement, direction, or purpose (e.g., parking, approaching, circling). If it's a delivery vehicle, mention the company."
 ```
 
 Prompts can also be overridden at the camera level to provide a more detailed prompt to the model about your specific camera, if you desire.

docs/docs/configuration/genai/config.md

@@ -43,9 +43,10 @@ The following models are recommended:
 
 | Model             | Notes                                                       |
 | ----------------- | ----------------------------------------------------------- |
-| `Intern3.5VL`     | Relatively fast with good vision comprehension
+| `qwen3-vl`        | Strong visual and situational understanding                 |
+| `Intern3.5VL`     | Relatively fast with good vision comprehension              |
 | `gemma3`          | Strong frame-to-frame understanding, slower inference times |
-| `qwen2.5vl`       | Fast but capable model with good vision comprehension       |
+| `qwen2.5-vl`      | Fast but capable model with good vision comprehension       |
 | `llava-phi3`      | Lightweight and fast model with vision comprehension        |
 
 :::note

docs/docs/configuration/genai/review_summaries.md

@@ -7,38 +7,95 @@ Generative AI can be used to automatically generate structured summaries of revi
 
 Requests for a summary are requested automatically to your AI provider for alert review items when the activity has ended, they can also be optionally enabled for detections as well.
 
-Generative AI review summaries can also be toggled dynamically for a camera via MQTT with the topic `frigate/<camera_name>/review_descriptions/set`. See the [MQTT documentation](/integrations/mqtt/#frigatecamera_namereviewdescriptionsset).
+Generative AI review summaries can also be toggled dynamically for a [camera via MQTT](/integrations/mqtt/#frigatecamera_namereviewdescriptionsset).
 
 ## Review Summary Usage and Best Practices
 
 Review summaries provide structured JSON responses that are saved for each review item:
 
 ```
-- `scene` (string): A full description including setting, entities, actions, and any plausible supported inferences.
-- `confidence` (float): 0-1 confidence in the analysis.
+- `title` (string): A concise, direct title that describes the purpose or overall action (e.g., "Person taking out trash", "Joe walking dog").
+- `scene` (string): A narrative description of what happens across the sequence from start to finish, including setting, detected objects, and their observable actions.
+- `confidence` (float): 0-1 confidence in the analysis. Higher confidence when objects/actions are clearly visible and context is unambiguous.
 - `other_concerns` (list): List of user-defined concerns that may need additional investigation.
 - `potential_threat_level` (integer): 0, 1, or 2 as defined below.
-
-Threat-level definitions:
-- 0 — Typical or expected activity for this location/time (includes residents, guests, or known animals engaged in normal activities, even if they glance around or scan surroundings).
-- 1 — Unusual or suspicious activity: At least one security-relevant behavior is present **and not explainable by a normal residential activity**.
-- 2 — Active or immediate threat: Breaking in, vandalism, aggression, weapon display.
 ```
 
-This will show in the UI as a list of concerns that each review item has along with the general description.
+This will show in multiple places in the UI to give additional context about each activity, and allow viewing more details when extra attention is required. Frigate's built in notifications will also automatically show the title and description when the data is available.
 
 ### Defining Typical Activity
 
-Each installation and even camera can have different parameters for what is considered suspicious activity. Frigate allows the `activity_context_prompt` to be defined globally and at the camera level, which allows you to define more specifically what should be considered normal activity. It is important that this is not overly specific as it can sway the output of the response. The default `activity_context_prompt` is below:
+Each installation and even camera can have different parameters for what is considered suspicious activity. Frigate allows the `activity_context_prompt` to be defined globally and at the camera level, which allows you to define more specifically what should be considered normal activity. It is important that this is not overly specific as it can sway the output of the response.
+
+<details>
+  <summary>Default Activity Context Prompt</summary>
 
 ```
-- **Zone context is critical**: Private enclosed spaces (back yards, back decks, fenced areas, inside garages) are resident territory where brief transient activity, routine tasks, and pet care are expected and normal. Front yards, driveways, and porches are semi-public but still resident spaces where deliveries, parking, and coming/going are routine. Consider whether the zone and activity align with normal residential use.
-- **Person + Pet = Normal Activity**: When both "Person" and "Dog" (or "Cat") are detected together in residential zones, this is routine pet care activity (walking, letting out, playing, supervising). Assign Level 0 unless there are OTHER strong suspicious behaviors present (like testing doors, taking items, etc.). A person with their pet in a residential zone is baseline normal activity.
-- Brief appearances in private zones (back yards, garages) are normal residential patterns.
-- Normal residential activity includes: residents, family members, guests, deliveries, services, maintenance workers, routine property use (parking, unloading, mail pickup, trash removal).
-- Brief movement with legitimate items (bags, packages, tools, equipment) in appropriate zones is routine.
+### Normal Activity Indicators (Level 0)
+- Known/verified people in any zone at any time
+- People with pets in residential areas
+- Deliveries or services during daytime/evening (6 AM - 10 PM): carrying packages to doors/porches, placing items, leaving
+- Services/maintenance workers with visible tools, uniforms, or service vehicles during daytime
+- Activity confined to public areas only (sidewalks, streets) without entering property at any time
+
+### Suspicious Activity Indicators (Level 1)
+- **Testing or attempting to open doors/windows/handles on vehicles or buildings** — ALWAYS Level 1 regardless of time or duration
+- **Unidentified person in private areas (driveways, near vehicles/buildings) during late night/early morning (11 PM - 5 AM)** — ALWAYS Level 1 regardless of activity or duration
+- Taking items that don't belong to them (packages, objects from porches/driveways)
+- Climbing or jumping fences/barriers to access property
+- Attempting to conceal actions or items from view
+- Prolonged loitering: remaining in same area without visible purpose throughout most of the sequence
+
+### Critical Threat Indicators (Level 2)
+- Holding break-in tools (crowbars, pry bars, bolt cutters)
+- Weapons visible (guns, knives, bats used aggressively)
+- Forced entry in progress
+- Physical aggression or violence
+- Active property damage or theft in progress
+
+### Assessment Guidance
+Evaluate in this order:
+
+1. **If person is verified/known** → Level 0 regardless of time or activity
+2. **If person is unidentified:**
+   - Check time: If late night/early morning (11 PM - 5 AM) AND in private areas (driveways, near vehicles/buildings) → Level 1
+   - Check actions: If testing doors/handles, taking items, climbing → Level 1
+   - Otherwise, if daytime/evening (6 AM - 10 PM) with clear legitimate purpose (delivery, service worker) → Level 0
+3. **Escalate to Level 2 if:** Weapons, break-in tools, forced entry in progress, violence, or active property damage visible (escalates from Level 0 or 1)
+
+The mere presence of an unidentified person in private areas during late night hours is inherently suspicious and warrants human review, regardless of what activity they appear to be doing or how brief the sequence is.
 ```
 
+</details>
+
+### Image Source
+
+By default, review summaries use preview images (cached preview frames) which have a lower resolution but use fewer tokens per image. For better image quality and more detailed analysis, you can configure Frigate to extract frames directly from recordings at a higher resolution:
+
+```yaml
+review:
+  genai:
+    enabled: true
+    image_source: recordings # Options: "preview" (default) or "recordings"
+```
+
+When using `recordings`, frames are extracted at 480px height while maintaining the camera's original aspect ratio, providing better detail for the LLM while being mindful of context window size. This is particularly useful for scenarios where fine details matter, such as identifying license plates, reading text, or analyzing distant objects.
+
+The number of frames sent to the LLM is dynamically calculated based on:
+
+- Your LLM provider's context window size
+- The camera's resolution and aspect ratio (ultrawide cameras like 32:9 use more tokens per image)
+- The image source (recordings use more tokens than preview images)
+
+Frame counts are automatically optimized to use ~98% of the available context window while capping at 20 frames maximum to ensure reasonable inference times. Note that using recordings will:
+
+- Provide higher quality images to the LLM (480p vs 180p preview images)
+- Use more tokens per image due to higher resolution
+- Result in fewer frames being sent for ultrawide cameras due to larger image size
+- Require that recordings are enabled for the camera
+
+If recordings are not available for a given time period, the system will automatically fall back to using preview frames.
+
 ### Additional Concerns
 
 Along with the concern of suspicious activity or immediate threat, you may have concerns such as animals in your garden or a gate being left open. These concerns can be configured so that the review summaries will make note of them if the activity requires additional review. For example:

docs/docs/configuration/license_plate_recognition.md

@@ -30,8 +30,7 @@ In the default mode, Frigate's LPR needs to first detect a `car` or `motorcycle`
 
 ## Minimum System Requirements
 
-License plate recognition works by running AI models locally on your system. The models are relatively lightweight and can run on your CPU or GPU, depending on your configuration. At least 4GB of RAM is required.
-
+License plate recognition works by running AI models locally on your system. The YOLOv9 plate detector model and the OCR models ([PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)) are relatively lightweight and can run on your CPU or GPU, depending on your configuration. At least 4GB of RAM is required.
 ## Configuration
 
 License plate recognition is disabled by default. Enable it in your config file:

docs/docs/configuration/live.md

@@ -174,7 +174,7 @@ For devices that support two way talk, Frigate can be configured to use the feat
 - Ensure you access Frigate via https (may require [opening port 8971](/frigate/installation/#ports)).
 - For the Home Assistant Frigate card, [follow the docs](http://card.camera/#/usage/2-way-audio) for the correct source.
 
-To use the Reolink Doorbell with two way talk, you should use the [recommended Reolink configuration](/configuration/camera_specific#reolink-doorbell)
+To use the Reolink Doorbell with two way talk, you should use the [recommended Reolink configuration](/configuration/camera_specific#reolink-cameras)
 
 As a starting point to check compatibility for your camera, view the list of cameras supported for two-way talk on the [go2rtc repository](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#two-way-audio). For cameras in the category `ONVIF Profile T`, you can use the [ONVIF Conformant Products Database](https://www.onvif.org/conformant-products/)'s FeatureList to check for the presence of `AudioOutput`. A camera that supports `ONVIF Profile T` _usually_ supports this, but due to inconsistent support, a camera that explicitly lists this feature may still not work. If no entry for your camera exists on the database, it is recommended not to buy it or to consult with the manufacturer's support on the feature availability.
 

docs/docs/configuration/object_detectors.md

@@ -253,11 +253,11 @@ 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 VPU hardware. 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 configure an OpenVINO detector, set the `"type"` attribute to `"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/2024/openvino-workflow/running-inference/inference-devices-and-modes.html). The most common devices are `CPU` and `GPU`. Currently, there is a known issue with using `AUTO`. For backwards compatibility, Frigate will attempt to use `GPU` if `AUTO` is set in your configuration.
+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`.
 
-OpenVINO is supported on 6th Gen Intel platforms (Skylake) and newer. It will also run on AMD CPUs despite having no official support for it. A supported Intel platform is required to use the `GPU` device with OpenVINO. For detailed system requirements, see [OpenVINO System Requirements](https://docs.openvino.ai/2024/about-openvino/release-notes-openvino/system-requirements.html)
+OpenVINO is supported on 6th Gen Intel platforms (Skylake) and newer. It will also run on AMD CPUs despite having no official support for it. A supported Intel platform is required to use the `GPU` or `NPU` device with OpenVINO. For detailed system requirements, see [OpenVINO System Requirements](https://docs.openvino.ai/2025/about-openvino/release-notes-openvino/system-requirements.html)
 
 :::tip
 
@@ -267,27 +267,39 @@ When using many cameras one detector may not be enough to keep up. Multiple dete
 detectors:
   ov_0:
     type: openvino
-    device: GPU
+    device: GPU # or NPU
   ov_1:
     type: openvino
-    device: GPU
+    device: GPU # or NPU
 ```
 
 :::
 
 ### OpenVINO Supported Models
 
+| Model                                 | GPU | NPU | Notes                                                        |
+| ------------------------------------- | --- | --- | ------------------------------------------------------------ |
+| [YOLOv9](#yolo-v3-v4-v7-v9)           | ✅  | ✅  | Recommended for GPU & NPU                                    |
+| [RF-DETR](#rf-detr)                   | ✅  | ✅  | Requires XE iGPU or Arc                                      |
+| [YOLO-NAS](#yolo-nas)                 | ✅  | ✅  |                                                              |
+| [MobileNet v2](#ssdlite-mobilenet-v2) | ✅  | ✅  | Fast and lightweight model, less accurate than larger models |
+| [YOLOX](#yolox)                       | ✅  |  ?  |                                                              |
+| [D-FINE](#d-fine)                     | ❌  | ❌  |                                                              |
+
 #### SSDLite MobileNet v2
 
 An OpenVINO model is provided in the container at `/openvino-model/ssdlite_mobilenet_v2.xml` and is used by this detector type by default. The model comes from Intel's Open Model Zoo [SSDLite MobileNet V2](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssdlite_mobilenet_v2) and is converted to an FP16 precision IR model.
 
+<details>
+  <summary>MobileNet v2 Config</summary>
+
 Use the model configuration shown below when using the OpenVINO detector with the default OpenVINO model:
 
 ```yaml
 detectors:
   ov:
     type: openvino
-    device: GPU
+    device: GPU # Or NPU
 
 model:
   width: 300
@@ -298,6 +310,8 @@ model:
   labelmap_path: /openvino-model/coco_91cl_bkgr.txt
 ```
 
+</details>
+
 #### YOLOX
 
 This detector also supports YOLOX. Frigate does not come with any YOLOX models preloaded, so you will need to supply your own models.
@@ -306,6 +320,9 @@ This detector also supports YOLOX. Frigate does not come with any YOLOX models p
 
 [YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. See [the models section](#downloading-yolo-nas-model) for more information on downloading the YOLO-NAS model for use in Frigate.
 
+<details>
+  <summary>YOLO-NAS Setup & Config</summary>
+
 After placing the downloaded onnx model in your config folder, you can use the following configuration:
 
 ```yaml
@@ -326,6 +343,8 @@ model:
 
 Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
 
+</details>
+
 #### YOLO (v3, v4, v7, v9)
 
 YOLOv3, YOLOv4, YOLOv7, and [YOLOv9](https://github.com/WongKinYiu/yolov9) models are supported, but not included by default.
@@ -336,6 +355,9 @@ The YOLO detector has been designed to support YOLOv3, YOLOv4, YOLOv7, and YOLOv
 
 :::
 
+<details>
+  <summary>YOLOv Setup & Config</summary>
+
 :::warning
 
 If you are using a Frigate+ YOLOv9 model, you should not define any of the below `model` parameters in your config except for `path`. See [the Frigate+ model docs](/plus/first_model#step-3-set-your-model-id-in-the-config) for more information on setting up your model.
@@ -348,7 +370,7 @@ After placing the downloaded onnx model in your config folder, you can use the f
 detectors:
   ov:
     type: openvino
-    device: GPU
+    device: GPU # or NPU
 
 model:
   model_type: yolo-generic
@@ -362,6 +384,8 @@ model:
 
 Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
 
+</details>
+
 #### RF-DETR
 
 [RF-DETR](https://github.com/roboflow/rf-detr) is a DETR based model. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-rf-detr-model) for more informatoin on downloading the RF-DETR model for use in Frigate.
@@ -372,6 +396,9 @@ Due to the size and complexity of the RF-DETR model, it is only recommended to b
 
 :::
 
+<details>
+  <summary>RF-DETR Setup & Config</summary>
+
 After placing the downloaded onnx model in your `config/model_cache` folder, you can use the following configuration:
 
 ```yaml
@@ -389,6 +416,8 @@ model:
   path: /config/model_cache/rfdetr.onnx
 ```
 
+</details>
+
 #### D-FINE
 
 [D-FINE](https://github.com/Peterande/D-FINE) is a DETR based model. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-d-fine-model) for more information on downloading the D-FINE model for use in Frigate.
@@ -399,6 +428,9 @@ Currently D-FINE models only run on OpenVINO in CPU mode, GPUs currently fail to
 
 :::
 
+<details>
+  <summary>D-FINE Setup & Config</summary>
+
 After placing the downloaded onnx model in your config/model_cache folder, you can use the following configuration:
 
 ```yaml
@@ -413,15 +445,17 @@ model:
   height: 640
   input_tensor: nchw
   input_dtype: float
-  path: /config/model_cache/dfine_s_obj2coco.onnx
+  path: /config/model_cache/dfine-s.onnx
   labelmap_path: /labelmap/coco-80.txt
 ```
 
 Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
 
+</details>
+
 ## Apple Silicon detector
 
-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`.
+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
 
@@ -609,12 +643,23 @@ detectors:
 
 ### ONNX Supported Models
 
+| Model                         | Nvidia GPU | AMD GPU | Notes                                               |
+| ----------------------------- | ---------- | ------- | --------------------------------------------------- |
+| [YOLOv9](#yolo-v3-v4-v7-v9-2) | ✅         | ✅      | Supports CUDA Graphs for optimal Nvidia performance |
+| [RF-DETR](#rf-detr)           | ✅         | ❌      | Supports CUDA Graphs for optimal Nvidia performance |
+| [YOLO-NAS](#yolo-nas-1)       | ⚠️         | ⚠️      | Not supported by CUDA Graphs                        |
+| [YOLOX](#yolox-1)             | ✅         | ✅      | Supports CUDA Graphs for optimal Nvidia performance |
+| [D-FINE](#d-fine)             | ⚠️         | ❌      | Not supported by CUDA Graphs                        |
+
 There is no default model provided, the following formats are supported:
 
 #### YOLO-NAS
 
 [YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. See [the models section](#downloading-yolo-nas-model) for more information on downloading the YOLO-NAS model for use in Frigate.
 
+<details>
+  <summary>YOLO-NAS Setup & Config</summary>
+
 :::warning
 
 If you are using a Frigate+ YOLO-NAS model, you should not define any of the below `model` parameters in your config except for `path`. See [the Frigate+ model docs](/plus/first_model#step-3-set-your-model-id-in-the-config) for more information on setting up your model.
@@ -638,6 +683,8 @@ model:
   labelmap_path: /labelmap/coco-80.txt
 ```
 
+</details>
+
 #### YOLO (v3, v4, v7, v9)
 
 YOLOv3, YOLOv4, YOLOv7, and [YOLOv9](https://github.com/WongKinYiu/yolov9) models are supported, but not included by default.
@@ -648,6 +695,9 @@ The YOLO detector has been designed to support YOLOv3, YOLOv4, YOLOv7, and YOLOv
 
 :::
 
+<details>
+  <summary>YOLOv Setup & Config</summary>
+
 :::warning
 
 If you are using a Frigate+ YOLOv9 model, you should not define any of the below `model` parameters in your config except for `path`. See [the Frigate+ model docs](/plus/first_model#step-3-set-your-model-id-in-the-config) for more information on setting up your model.
@@ -671,12 +721,17 @@ model:
   labelmap_path: /labelmap/coco-80.txt
 ```
 
+</details>
+
 Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
 
 #### YOLOx
 
 [YOLOx](https://github.com/Megvii-BaseDetection/YOLOX) models are supported, but not included by default. See [the models section](#downloading-yolo-models) for more information on downloading the YOLOx model for use in Frigate.
 
+<details>
+  <summary>YOLOx Setup & Config</summary>
+
 After placing the downloaded onnx model in your config folder, you can use the following configuration:
 
 ```yaml
@@ -696,10 +751,15 @@ model:
 
 Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
 
+</details>
+
 #### RF-DETR
 
 [RF-DETR](https://github.com/roboflow/rf-detr) is a DETR based model. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-rf-detr-model) for more information on downloading the RF-DETR model for use in Frigate.
 
+<details>
+  <summary>RF-DETR Setup & Config</summary>
+
 After placing the downloaded onnx model in your `config/model_cache` folder, you can use the following configuration:
 
 ```yaml
@@ -716,10 +776,15 @@ model:
   path: /config/model_cache/rfdetr.onnx
 ```
 
+</details>
+
 #### D-FINE
 
 [D-FINE](https://github.com/Peterande/D-FINE) is a DETR based model. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-d-fine-model) for more information on downloading the D-FINE model for use in Frigate.
 
+<details>
+  <summary>D-FINE Setup & Config</summary>
+
 After placing the downloaded onnx model in your `config/model_cache` folder, you can use the following configuration:
 
 ```yaml
@@ -737,6 +802,8 @@ model:
   labelmap_path: /labelmap/coco-80.txt
 ```
 
+</details>
+
 Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
 
 ## CPU Detector (not recommended)
@@ -856,16 +923,16 @@ detectors:
 
 model:
   model_type: yolonas
-  width: 320   # (Can be set to 640 for higher resolution)
-  height: 320  # (Can be set to 640 for higher resolution)
+  width: 320 # (Can be set to 640 for higher resolution)
+  height: 320 # (Can be set to 640 for higher resolution)
   input_tensor: nchw
   input_dtype: float
   labelmap_path: /labelmap/coco-80.txt
   # Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
   # path: /config/yolonas.zip
-          # The .zip file must contain:
-          # ├── yolonas.dfp          (a file ending with .dfp)
-          # └── yolonas_post.onnx    (optional; only if the model includes a cropped post-processing network)
+  # The .zip file must contain:
+  # ├── yolonas.dfp          (a file ending with .dfp)
+  # └── yolonas_post.onnx    (optional; only if the model includes a cropped post-processing network)
 ```
 
 #### YOLOv9
@@ -884,16 +951,16 @@ detectors:
 
 model:
   model_type: yolo-generic
-  width: 320   # (Can be set to 640 for higher resolution)
-  height: 320  # (Can be set to 640 for higher resolution)
+  width: 320 # (Can be set to 640 for higher resolution)
+  height: 320 # (Can be set to 640 for higher resolution)
   input_tensor: nchw
   input_dtype: float
   labelmap_path: /labelmap/coco-80.txt
   # Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
   # path: /config/yolov9.zip
-          # The .zip file must contain:
-          # ├── yolov9.dfp          (a file ending with .dfp)
-          # └── yolov9_post.onnx    (optional; only if the model includes a cropped post-processing network)
+  # The .zip file must contain:
+  # ├── yolov9.dfp          (a file ending with .dfp)
+  # └── yolov9_post.onnx    (optional; only if the model includes a cropped post-processing network)
 ```
 
 #### YOLOX
@@ -919,8 +986,8 @@ model:
   labelmap_path: /labelmap/coco-80.txt
   # Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
   # path: /config/yolox.zip
-          # The .zip file must contain:
-          # ├── yolox.dfp          (a file ending with .dfp)
+  # The .zip file must contain:
+  # ├── yolox.dfp          (a file ending with .dfp)
 ```
 
 #### SSDLite MobileNet v2
@@ -946,9 +1013,9 @@ model:
   labelmap_path: /labelmap/coco-80.txt
   # Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
   # path: /config/ssdlite_mobilenet.zip
-          # The .zip file must contain:
-          # ├── ssdlite_mobilenet.dfp          (a file ending with .dfp)
-          # └── ssdlite_mobilenet_post.onnx    (optional; only if the model includes a cropped post-processing network)
+  # The .zip file must contain:
+  # ├── ssdlite_mobilenet.dfp          (a file ending with .dfp)
+  # └── ssdlite_mobilenet_post.onnx    (optional; only if the model includes a cropped post-processing network)
 ```
 
 #### Using a Custom Model
@@ -968,18 +1035,19 @@ To use your own model:
 For detailed instructions on compiling models, refer to the [MemryX Compiler](https://developer.memryx.com/tools/neural_compiler.html#usage) docs and [Tutorials](https://developer.memryx.com/tutorials/tutorials.html).
 
 ```yaml
-  # The detector automatically selects the default model if nothing is provided in the config.
-  #
-  # Optionally, you can specify a local model path as a .zip file to override the default.
-  # If a local path is provided and the file exists, it will be used instead of downloading.
-  #
-  # Example:
-  # path: /config/yolonas.zip
-  #
-  # The .zip file must contain:
-  # ├── yolonas.dfp          (a file ending with .dfp)
-  # └── yolonas_post.onnx    (optional; only if the model includes a cropped post-processing network)
+# The detector automatically selects the default model if nothing is provided in the config.
+#
+# Optionally, you can specify a local model path as a .zip file to override the default.
+# If a local path is provided and the file exists, it will be used instead of downloading.
+#
+# Example:
+# path: /config/yolonas.zip
+#
+# The .zip file must contain:
+# ├── yolonas.dfp          (a file ending with .dfp)
+# └── yolonas_post.onnx    (optional; only if the model includes a cropped post-processing network)
 ```
+
 ---
 
 ## NVidia TensorRT Detector
@@ -1087,16 +1155,16 @@ A synap model is provided in the container at /mobilenet.synap and is used by th
 Use the model configuration shown below when using the synaptics detector with the default synap model:
 
 ```yaml
-detectors:  # required
-  synap_npu:  # required
-    type: synaptics  # required
+detectors: # required
+  synap_npu: # required
+    type: synaptics # required
 
-model:  # required
-  path: /synaptics/mobilenet.synap  # required
-  width: 224  # required
-  height: 224  # required
-  tensor_format: nhwc  # default value (optional. If you change the model, it is required)
-  labelmap_path: /labelmap/coco-80.txt  # required
+model: # required
+  path: /synaptics/mobilenet.synap # required
+  width: 224 # required
+  height: 224 # required
+  tensor_format: nhwc # default value (optional. If you change the model, it is required)
+  labelmap_path: /labelmap/coco-80.txt # required
 ```
 
 ## Rockchip platform
@@ -1270,97 +1338,101 @@ Explanation of the paramters:
 
 ## DeGirum
 
-DeGirum is a detector that can use any type of hardware listed on [their website](https://hub.degirum.com). DeGirum can be used with local hardware through a DeGirum AI Server, or through the use of `@local`. You can also connect directly to DeGirum's AI Hub to run inferences. **Please Note:** This detector *cannot* be used for commercial purposes.
+DeGirum is a detector that can use any type of hardware listed on [their website](https://hub.degirum.com). DeGirum can be used with local hardware through a DeGirum AI Server, or through the use of `@local`. You can also connect directly to DeGirum's AI Hub to run inferences. **Please Note:** This detector _cannot_ be used for commercial purposes.
 
 ### Configuration
 
 #### AI Server Inference
 
 Before starting with the config file for this section, you must first launch an AI server. DeGirum has an AI server ready to use as a docker container. Add this to your `docker-compose.yml` to get started:
+
 ```yaml
 degirum_detector:
-    container_name: degirum
-    image: degirum/aiserver:latest
-    privileged: true
-    ports:
-      - "8778:8778"
+  container_name: degirum
+  image: degirum/aiserver:latest
+  privileged: true
+  ports:
+    - "8778:8778"
 ```
+
 All supported hardware will automatically be found on your AI server host as long as relevant runtimes and drivers are properly installed on your machine. Refer to [DeGirum's docs site](https://docs.degirum.com/pysdk/runtimes-and-drivers) if you have any trouble.
 
 Once completed, changing the `config.yml` file is simple.
+
 ```yaml
 degirum_detector:
-    type: degirum
-    location: degirum # Set to service name (degirum_detector), container_name (degirum), or a host:port (192.168.29.4:8778)
-    zoo: degirum/public # DeGirum's public model zoo. Zoo name should be in format "workspace/zoo_name". degirum/public is available to everyone, so feel free to use it if you don't know where to start. If you aren't pulling a model from the AI Hub, leave this and 'token' blank.
-    token: dg_example_token # For authentication with the AI Hub. Get this token through the "tokens" section on the main page of the [AI Hub](https://hub.degirum.com). This can be left blank if you're pulling a model from the public zoo and running inferences on your local hardware using @local or a local DeGirum AI Server
-```
-Setting up a model in the `config.yml` is similar to setting up an AI server.
-You can set it to:
-- A model listed on the [AI Hub](https://hub.degirum.com), given that the correct zoo name is listed in your detector
-    - If this is what you choose to do, the correct model will be downloaded onto your machine before running.
-- A local directory acting as a zoo. See DeGirum's docs site [for more information](https://docs.degirum.com/pysdk/user-guide-pysdk/organizing-models#model-zoo-directory-structure).
-- A path to some model.json.
-```yaml
-model:
-    path: ./mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1 # directory to model .json and file
-    width: 300 # width is in the model name as the first number in the "int"x"int" section
-    height: 300 # height is in the model name as the second number in the "int"x"int" section
-    input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
+  type: degirum
+  location: degirum # Set to service name (degirum_detector), container_name (degirum), or a host:port (192.168.29.4:8778)
+  zoo: degirum/public # DeGirum's public model zoo. Zoo name should be in format "workspace/zoo_name". degirum/public is available to everyone, so feel free to use it if you don't know where to start. If you aren't pulling a model from the AI Hub, leave this and 'token' blank.
+  token: dg_example_token # For authentication with the AI Hub. Get this token through the "tokens" section on the main page of the [AI Hub](https://hub.degirum.com). This can be left blank if you're pulling a model from the public zoo and running inferences on your local hardware using @local or a local DeGirum AI Server
 ```
 
+Setting up a model in the `config.yml` is similar to setting up an AI server.
+You can set it to:
+
+- A model listed on the [AI Hub](https://hub.degirum.com), given that the correct zoo name is listed in your detector
+  - If this is what you choose to do, the correct model will be downloaded onto your machine before running.
+- A local directory acting as a zoo. See DeGirum's docs site [for more information](https://docs.degirum.com/pysdk/user-guide-pysdk/organizing-models#model-zoo-directory-structure).
+- A path to some model.json.
+
+```yaml
+model:
+  path: ./mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1 # directory to model .json and file
+  width: 300 # width is in the model name as the first number in the "int"x"int" section
+  height: 300 # height is in the model name as the second number in the "int"x"int" section
+  input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
+```
 
 #### Local Inference
 
 It is also possible to eliminate the need for an AI server and run the hardware directly. The benefit of this approach is that you eliminate any bottlenecks that occur when transferring prediction results from the AI server docker container to the frigate one. However, the method of implementing local inference is different for every device and hardware combination, so it's usually more trouble than it's worth. A general guideline to achieve this would be:
+
 1. Ensuring that the frigate docker container has the runtime you want to use. So for instance, running `@local` for Hailo means making sure the container you're using has the Hailo runtime installed.
 2. To double check the runtime is detected by the DeGirum detector, make sure the `degirum sys-info` command properly shows whatever runtimes you mean to install.
 3. Create a DeGirum detector in your `config.yml` file.
 
 ```yaml
 degirum_detector:
-    type: degirum
-    location: "@local" # For accessing AI Hub devices and models
-    zoo: degirum/public # DeGirum's public model zoo. Zoo name should be in format "workspace/zoo_name". degirum/public is available to everyone, so feel free to use it if you don't know where to start.
-    token: dg_example_token # For authentication with the AI Hub. Get this token through the "tokens" section on the main page of the [AI Hub](https://hub.degirum.com). This can be left blank if you're pulling a model from the public zoo and running inferences on your local hardware using @local or a local DeGirum AI Server
-
+  type: degirum
+  location: "@local" # For accessing AI Hub devices and models
+  zoo: degirum/public # DeGirum's public model zoo. Zoo name should be in format "workspace/zoo_name". degirum/public is available to everyone, so feel free to use it if you don't know where to start.
+  token: dg_example_token # For authentication with the AI Hub. Get this token through the "tokens" section on the main page of the [AI Hub](https://hub.degirum.com). This can be left blank if you're pulling a model from the public zoo and running inferences on your local hardware using @local or a local DeGirum AI Server
 ```
 
 Once `degirum_detector` is setup, you can choose a model through 'model' section in the `config.yml` file.
 
 ```yaml
 model:
-    path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
-    width: 300 # width is in the model name as the first number in the "int"x"int" section
-    height: 300 # height is in the model name as the second number in the "int"x"int" section
-    input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
+  path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
+  width: 300 # width is in the model name as the first number in the "int"x"int" section
+  height: 300 # height is in the model name as the second number in the "int"x"int" section
+  input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
 ```
 
-
 #### AI Hub Cloud Inference
 
 If you do not possess whatever hardware you want to run, there's also the option to run cloud inferences. Do note that your detection fps might need to be lowered as network latency does significantly slow down this method of detection. For use with Frigate, we highly recommend using a local AI server as described above. To set up cloud inferences,
+
 1. Sign up at [DeGirum's AI Hub](https://hub.degirum.com).
 2. Get an access token.
 3. Create a DeGirum detector in your `config.yml` file.
 
 ```yaml
 degirum_detector:
-    type: degirum
-    location: "@cloud" # For accessing AI Hub devices and models
-    zoo: degirum/public # DeGirum's public model zoo. Zoo name should be in format "workspace/zoo_name". degirum/public is available to everyone, so feel free to use it if you don't know where to start.
-    token: dg_example_token # For authentication with the AI Hub. Get this token through the "tokens" section on the main page of the (AI Hub)[https://hub.degirum.com).
-
+  type: degirum
+  location: "@cloud" # For accessing AI Hub devices and models
+  zoo: degirum/public # DeGirum's public model zoo. Zoo name should be in format "workspace/zoo_name". degirum/public is available to everyone, so feel free to use it if you don't know where to start.
+  token: dg_example_token # For authentication with the AI Hub. Get this token through the "tokens" section on the main page of the (AI Hub)[https://hub.degirum.com).
 ```
 
 Once `degirum_detector` is setup, you can choose a model through 'model' section in the `config.yml` file.
 
 ```yaml
 model:
-    path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
-    width: 300 # width is in the model name as the first number in the "int"x"int" section
-    height: 300 # height is in the model name as the second number in the "int"x"int" section
-    input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
+  path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
+  width: 300 # width is in the model name as the first number in the "int"x"int" section
+  height: 300 # height is in the model name as the second number in the "int"x"int" section
+  input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
 ```
 
 # Models
@@ -1383,7 +1455,7 @@ COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
 WORKDIR /dfine
 RUN git clone https://github.com/Peterande/D-FINE.git .
 RUN uv pip install --system -r requirements.txt
-RUN uv pip install --system onnx onnxruntime onnxsim
+RUN uv pip install --system onnx onnxruntime onnxsim onnxscript
 # Create output directory and download checkpoint
 RUN mkdir -p output
 ARG MODEL_SIZE
@@ -1407,9 +1479,9 @@ FROM python:3.11 AS build
 RUN apt-get update && apt-get install --no-install-recommends -y libgl1 && rm -rf /var/lib/apt/lists/*
 COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
 WORKDIR /rfdetr
-RUN uv pip install --system rfdetr onnx onnxruntime onnxsim onnx-graphsurgeon
+RUN uv pip install --system rfdetr[onnxexport] torch==2.8.0 onnxscript
 ARG MODEL_SIZE
-RUN python3 -c "from rfdetr import RFDETR${MODEL_SIZE}; x = RFDETR${MODEL_SIZE}(resolution=320); x.export()"
+RUN python3 -c "from rfdetr import RFDETR${MODEL_SIZE}; x = RFDETR${MODEL_SIZE}(resolution=320); x.export(simplify=True)"
 FROM scratch
 ARG MODEL_SIZE
 COPY --from=build /rfdetr/output/inference_model.onnx /rfdetr-${MODEL_SIZE}.onnx
@@ -1457,7 +1529,7 @@ COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
 WORKDIR /yolov9
 ADD https://github.com/WongKinYiu/yolov9.git .
 RUN uv pip install --system -r requirements.txt
-RUN uv pip install --system onnx==1.18.0 onnxruntime onnx-simplifier>=0.4.1
+RUN uv pip install --system onnx==1.18.0 onnxruntime onnx-simplifier>=0.4.1 onnxscript
 ARG MODEL_SIZE
 ARG IMG_SIZE
 ADD https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-${MODEL_SIZE}-converted.pt yolov9-${MODEL_SIZE}.pt

docs/docs/configuration/reference.md

@@ -240,6 +240,8 @@ birdseye:
     scaling_factor: 2.0
     # Optional: Maximum number of cameras to show at one time, showing the most recent (default: show all cameras)
     max_cameras: 1
+  # Optional: Frames-per-second to re-send the last composed Birdseye frame when idle (no motion or active updates). (default: shown below)
+  idle_heartbeat_fps: 0.0
 
 # Optional: ffmpeg configuration
 # More information about presets at https://docs.frigate.video/configuration/ffmpeg_presets
@@ -427,6 +429,15 @@ review:
     alerts: True
     # Optional: Enable GenAI review summaries for detections (default: shown below)
     detections: False
+    # Optional: Activity Context Prompt to give context to the GenAI what activity is and is not suspicious.
+    # It is important to be direct and detailed. See documentation for the default prompt structure.
+    activity_context_prompt: """Define what is and is not suspicious
+"""
+    # Optional: Image source for GenAI (default: preview)
+    # Options: "preview" (uses cached preview frames at ~180p) or "recordings" (extracts frames from recordings at 480p)
+    # Using "recordings" provides better image quality but uses more tokens per image.
+    # Frame count is automatically calculated based on context window size, aspect ratio, and image source (capped at 20 frames).
+    image_source: preview
     # Optional: Additional concerns that the GenAI should make note of (default: None)
     additional_concerns:
       - Animals in the garden
@@ -628,7 +639,7 @@ face_recognition:
   # Optional: Min face recognitions for the sub label to be applied to the person object (default: shown below)
   min_faces: 1
   # Optional: Number of images of recognized faces to save for training (default: shown below)
-  save_attempts: 100
+  save_attempts: 200
   # Optional: Apply a blur quality filter to adjust confidence based on the blur level of the image (default: shown below)
   blur_confidence_filter: True
   # Optional: Set the model size used face recognition. (default: shown below)
@@ -669,20 +680,18 @@ lpr:
   # Optional: List of regex replacement rules to normalize detected plates (default: shown below)
   replace_rules: {}
 
-# Optional: Configuration for AI generated tracked object descriptions
+# Optional: Configuration for AI / LLM provider
 # WARNING: Depending on the provider, this will send thumbnails over the internet
-# to Google or OpenAI's LLMs to generate descriptions. It can be overridden at
-# the camera level (enabled: False) to enhance privacy for indoor cameras.
+# to Google or OpenAI's LLMs to generate descriptions. GenAI features can be configured at
+# the camera level to enhance privacy for indoor cameras.
 genai:
-  # Optional: Enable AI description generation (default: shown below)
-  enabled: False
-  # Required if enabled: Provider must be one of ollama, gemini, or openai
+  # Required: Provider must be one of ollama, gemini, or openai
   provider: ollama
   # Required if provider is ollama. May also be used for an OpenAI API compatible backend with the openai provider.
   base_url: http://localhost::11434
   # Required if gemini or openai
   api_key: "{FRIGATE_GENAI_API_KEY}"
-  # Required if enabled: The model to use with the provider.
+  # Required: The model to use with the provider.
   model: gemini-1.5-flash
   # Optional additional args to pass to the GenAI Provider (default: None)
   provider_options:
@@ -920,10 +929,13 @@ cameras:
         type: thumbnail
         # Reference data for matching, either an event ID for `thumbnail` or a text string for `description`. (default: none)
         data: 1751565549.853251-b69j73
-        # Similarity threshold for triggering. (default: none)
-        threshold: 0.7
+        # Similarity threshold for triggering. (default: shown below)
+        threshold: 0.8
         # List of actions to perform when the trigger fires. (default: none)
-        # Available options: `notification` (send a webpush notification)
+        # Available options:
+        # - `notification` (send a webpush notification)
+        # - `sub_label` (add trigger friendly name as a sub label to the triggering tracked object)
+        # - `attribute` (add trigger's name and similarity score as a data attribute to the triggering tracked object)
         actions:
           - notification
 

docs/docs/configuration/restream.md

@@ -24,6 +24,11 @@ birdseye:
   restream: True
 ```
 
+:::tip 
+
+To improve connection speed when using Birdseye via restream you can enable a small idle heartbeat by setting `birdseye.idle_heartbeat_fps` to a low value (e.g. `1–2`).  This makes Frigate periodically push the last frame even when no motion is detected, reducing initial connection latency.
+
+:::
 ### Securing Restream With Authentication
 
 The go2rtc restream can be secured with RTSP based username / password authentication. Ex:

docs/docs/configuration/semantic_search.md

@@ -119,7 +119,7 @@ Semantic Search must be enabled to use Triggers.
 
 ### Configuration
 
-Triggers are defined within the `semantic_search` configuration for each camera in your Frigate configuration file or through the UI. Each trigger consists of a `friendly_name`, a `type` (either `thumbnail` or `description`), a `data` field (the reference image event ID or text), a `threshold` for similarity matching, and a list of `actions` to perform when the trigger fires.
+Triggers are defined within the `semantic_search` configuration for each camera in your Frigate configuration file or through the UI. Each trigger consists of a `friendly_name`, a `type` (either `thumbnail` or `description`), a `data` field (the reference image event ID or text), a `threshold` for similarity matching, and a list of `actions` to perform when the trigger fires - `notification`, `sub_label`, and `attribute`.
 
 Triggers are best configured through the Frigate UI.
 
@@ -128,17 +128,20 @@ Triggers are best configured through the Frigate UI.
 1. Navigate to the **Settings** page and select the **Triggers** tab.
 2. Choose a camera from the dropdown menu to view or manage its triggers.
 3. Click **Add Trigger** to create a new trigger or use the pencil icon to edit an existing one.
-4. In the **Create Trigger** dialog:
-   - Enter a **Name** for the trigger (e.g., "red_car_alert").
+4. In the **Create Trigger** wizard:
+   - 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.
    - Set the **Threshold** for similarity matching.
    - Select **Actions** to perform when the trigger fires.
+     If native webpush notifications are enabled, check the `Send Notification` box to send a notification.
+     Check the `Add Sub Label` box to add the trigger's friendly name as a sub label to any triggering tracked objects.
+     Check the `Add Attribute` box to add the trigger's internal ID (e.g., "red_car_alert") to a data attribute on the tracked object that can be processed via the API or MQTT.
 5. Save the trigger to update the configuration and store the embedding in the database.
 
-When a trigger fires, the UI highlights the trigger with a blue outline for 3 seconds for easy identification.
+When a trigger fires, the UI highlights the trigger with a blue dot for 3 seconds for easy identification.
 
 ### Usage and Best Practices
 

docs/docs/frigate/hardware.md

@@ -78,7 +78,7 @@ Frigate supports multiple different detectors that work on different types of ha
 
 **Intel**
 
-- [OpenVino](#openvino---intel): OpenVino can run on Intel Arc GPUs, Intel integrated GPUs, and Intel CPUs to provide efficient object detection.
+- [OpenVino](#openvino---intel): OpenVino can run on Intel Arc GPUs, Intel integrated GPUs, and Intel NPUs to provide efficient object detection.
   - [Supports majority of model architectures](../../configuration/object_detectors#openvino-supported-models)
   - Runs best with tiny, small, or medium models
 
@@ -142,6 +142,7 @@ The OpenVINO detector type is able to run on:
 
 - 6th Gen Intel Platforms and newer that have an iGPU
 - x86 hosts with an Intel Arc GPU
+- Intel NPUs
 - Most modern AMD CPUs (though this is officially not supported by Intel)
 - x86 & Arm64 hosts via CPU (generally not recommended)
 
@@ -166,7 +167,8 @@ Inference speeds vary greatly depending on the CPU or GPU used, some known examp
 | Intel UHD 770  | ~ 15 ms                    | t-320: ~ 16 ms s-320: ~ 20 ms s-640: ~ 40 ms      | 320: ~ 20 ms 640: ~ 46 ms |                        |                                    |
 | Intel N100     | ~ 15 ms                    | s-320: 30 ms                                      | 320: ~ 25 ms              |                        | Can only run one detector instance |
 | Intel N150     | ~ 15 ms                    | t-320: 16 ms s-320: 24 ms                         |                           |                        |                                    |
-| Intel Iris XE  | ~ 10 ms                    | s-320: 12 ms s-640: 30 ms                         | 320: ~ 18 ms 640: ~ 50 ms |                        |                                    |
+| Intel Iris XE  | ~ 10 ms                    | t-320: 6 ms t-640: 14 ms s-320: 8 ms s-640: 16 ms | 320: ~ 10 ms 640: ~ 20 ms | 320-n: 33 ms           |                                    |
+| Intel NPU      | ~ 6 ms                     | s-320: 11 ms                                      | 320: ~ 14 ms 640: ~ 34 ms | 320-n: 40 ms           |                                    |
 | Intel Arc A310 | ~ 5 ms                     | t-320: 7 ms t-640: 11 ms s-320: 8 ms s-640: 15 ms | 320: ~ 8 ms 640: ~ 14 ms  |                        |                                    |
 | Intel Arc A380 | ~ 6 ms                     |                                                   | 320: ~ 10 ms 640: ~ 22 ms | 336: 20 ms 448: 27 ms  |                                    |
 | Intel Arc A750 | ~ 4 ms                     |                                                   | 320: ~ 8 ms               |                        |                                    |

docs/docs/frigate/installation.md

@@ -304,7 +304,8 @@ services:
       - /dev/bus/usb:/dev/bus/usb # Passes the USB Coral, needs to be modified for other versions
       - /dev/apex_0:/dev/apex_0 # Passes a PCIe Coral, follow driver instructions here https://coral.ai/docs/m2/get-started/#2a-on-linux
       - /dev/video11:/dev/video11 # For Raspberry Pi 4B
-      - /dev/dri/renderD128:/dev/dri/renderD128 # For intel hwaccel, needs to be updated for your hardware
+      - /dev/dri/renderD128:/dev/dri/renderD128 # AMD / Intel GPU, needs to be updated for your hardware
+      - /dev/accel:/dev/accel # Intel NPU
     volumes:
       - /etc/localtime:/etc/localtime:ro
       - /path/to/your/config:/config

docs/docs/frigate/updating.md

@@ -5,7 +5,7 @@ title: Updating
 
 # Updating Frigate
 
-The current stable version of Frigate is **0.16.1**. The release notes and any breaking changes for this version can be found on the [Frigate GitHub releases page](https://github.com/blakeblackshear/frigate/releases/tag/v0.16.1).
+The current stable version of Frigate is **0.16.2**. The release notes and any breaking changes for this version can be found on the [Frigate GitHub releases page](https://github.com/blakeblackshear/frigate/releases/tag/v0.16.2).
 
 Keeping Frigate up to date ensures you benefit from the latest features, performance improvements, and bug fixes. The update process varies slightly depending on your installation method (Docker, Home Assistant Addon, etc.). Below are instructions for the most common setups.
 
@@ -33,21 +33,21 @@ If you’re running Frigate via Docker (recommended method), follow these steps:
 2. **Update and Pull the Latest Image**:
 
    - If using Docker Compose:
-     - Edit your `docker-compose.yml` file to specify the desired version tag (e.g., `0.16.1` instead of `0.15.2`). For example:
+     - Edit your `docker-compose.yml` file to specify the desired version tag (e.g., `0.16.2` instead of `0.15.2`). For example:
        ```yaml
        services:
          frigate:
-           image: ghcr.io/blakeblackshear/frigate:0.16.1
+           image: ghcr.io/blakeblackshear/frigate:0.16.2
        ```
      - Then pull the image:
        ```bash
-       docker pull ghcr.io/blakeblackshear/frigate:0.16.1
+       docker pull ghcr.io/blakeblackshear/frigate:0.16.2
        ```
      - **Note for `stable` Tag Users**: If your `docker-compose.yml` uses the `stable` tag (e.g., `ghcr.io/blakeblackshear/frigate:stable`), you don’t need to update the tag manually. The `stable` tag always points to the latest stable release after pulling.
    - If using `docker run`:
-     - Pull the image with the appropriate tag (e.g., `0.16.1`, `0.16.1-tensorrt`, or `stable`):
+     - Pull the image with the appropriate tag (e.g., `0.16.2`, `0.16.2-tensorrt`, or `stable`):
        ```bash
-       docker pull ghcr.io/blakeblackshear/frigate:0.16.1
+       docker pull ghcr.io/blakeblackshear/frigate:0.16.2
        ```
 
 3. **Start the Container**:

docs/docs/integrations/mqtt.md

@@ -161,7 +161,14 @@ Message published for updates to tracked object metadata, for example:
 
 ### `frigate/reviews`
 
-Message published for each changed review item. The first message is published when the `detection` or `alert` is initiated. When additional objects are detected or when a zone change occurs, it will publish a, `update` message with the same id. When the review activity has ended a final `end` message is published.
+Message published for each changed review item. The first message is published when the `detection` or `alert` is initiated. 
+
+An `update` with the same ID will be published when:
+- The severity changes from `detection` to `alert`
+- Additional objects are detected
+- An object is recognized via face, lpr, etc.
+
+When the review activity has ended a final `end` message is published.
 
 ```json
 {

docs/docs/plus/annotating.md

@@ -42,6 +42,7 @@ Misidentified objects should have a correct label added. For example, if a perso
 | `w`               | Add box                       |
 | `d`               | Toggle difficult              |
 | `s`               | Switch to the next label      |
+| `Shift + s`       | Switch to the previous label  |
 | `tab`             | Select next largest box       |
 | `del`             | Delete current box            |
 | `esc`             | Deselect/Cancel               |

frigate/api/app.py

@@ -387,20 +387,28 @@ def config_set(request: Request, body: AppConfigSetBody):
         old_config: FrigateConfig = request.app.frigate_config
         request.app.frigate_config = config
 
-        if body.update_topic and body.update_topic.startswith("config/cameras/"):
-            _, _, camera, field = body.update_topic.split("/")
+        if body.update_topic:
+            if body.update_topic.startswith("config/cameras/"):
+                _, _, camera, field = body.update_topic.split("/")
 
-            if field == "add":
-                settings = config.cameras[camera]
-            elif field == "remove":
-                settings = old_config.cameras[camera]
+                if field == "add":
+                    settings = config.cameras[camera]
+                elif field == "remove":
+                    settings = old_config.cameras[camera]
+                else:
+                    settings = config.get_nested_object(body.update_topic)
+
+                request.app.config_publisher.publish_update(
+                    CameraConfigUpdateTopic(CameraConfigUpdateEnum[field], camera),
+                    settings,
+                )
             else:
+                # Handle nested config updates (e.g., config/classification/custom/{name})
                 settings = config.get_nested_object(body.update_topic)
-
-            request.app.config_publisher.publish_update(
-                CameraConfigUpdateTopic(CameraConfigUpdateEnum[field], camera),
-                settings,
-            )
+                if settings:
+                    request.app.config_publisher.publisher.publish(
+                        body.update_topic, settings
+                    )
 
     return JSONResponse(
         content=(
@@ -688,7 +696,11 @@ def timeline(camera: str = "all", limit: int = 100, source_id: Optional[str] = N
         clauses.append((Timeline.camera == camera))
 
     if source_id:
-        clauses.append((Timeline.source_id == source_id))
+        source_ids = [sid.strip() for sid in source_id.split(",")]
+        if len(source_ids) == 1:
+            clauses.append((Timeline.source_id == source_ids[0]))
+        else:
+            clauses.append((Timeline.source_id.in_(source_ids)))
 
     if len(clauses) == 0:
         clauses.append((True))

frigate/api/auth.py

@@ -35,6 +35,23 @@ logger = logging.getLogger(__name__)
 router = APIRouter(tags=[Tags.auth])
 
 
+@router.get("/auth/first_time_login")
+def first_time_login(request: Request):
+    """Return whether the admin first-time login help flag is set in config.
+
+    This endpoint is intentionally unauthenticated so the login page can
+    query it before a user is authenticated.
+    """
+    auth_config = request.app.frigate_config.auth
+
+    return JSONResponse(
+        content={
+            "admin_first_time_login": auth_config.admin_first_time_login
+            or auth_config.reset_admin_password
+        }
+    )
+
+
 class RateLimiter:
     _limit = ""
 
@@ -515,6 +532,11 @@ def login(request: Request, body: AppPostLoginBody):
         set_jwt_cookie(
             response, JWT_COOKIE_NAME, encoded_jwt, expiration, JWT_COOKIE_SECURE
         )
+        # Clear admin_first_time_login flag after successful admin login so the
+        # UI stops showing the first-time login documentation link.
+        if role == "admin":
+            request.app.frigate_config.auth.admin_first_time_login = False
+
         return response
     return JSONResponse(content={"message": "Login failed"}, status_code=401)
 

frigate/api/camera.py

@@ -199,19 +199,30 @@ def ffprobe(request: Request, paths: str = "", detailed: bool = False):
             request.app.frigate_config.ffmpeg, path.strip(), detailed=detailed
         )
 
-        result = {
-            "return_code": ffprobe.returncode,
-            "stderr": (
-                ffprobe.stderr.decode("unicode_escape").strip()
-                if ffprobe.returncode != 0
-                else ""
-            ),
-            "stdout": (
-                json.loads(ffprobe.stdout.decode("unicode_escape").strip())
-                if ffprobe.returncode == 0
-                else ""
-            ),
-        }
+        if ffprobe.returncode != 0:
+            try:
+                stderr_decoded = ffprobe.stderr.decode("utf-8")
+            except UnicodeDecodeError:
+                try:
+                    stderr_decoded = ffprobe.stderr.decode("unicode_escape")
+                except Exception:
+                    stderr_decoded = str(ffprobe.stderr)
+
+            stderr_lines = [
+                line.strip() for line in stderr_decoded.split("\n") if line.strip()
+            ]
+
+            result = {
+                "return_code": ffprobe.returncode,
+                "stderr": stderr_lines,
+                "stdout": "",
+            }
+        else:
+            result = {
+                "return_code": ffprobe.returncode,
+                "stderr": [],
+                "stdout": json.loads(ffprobe.stdout.decode("unicode_escape").strip()),
+            }
 
         # Add detailed metadata if requested and probe was successful
         if detailed and ffprobe.returncode == 0 and result["stdout"]:

frigate/api/classification.py

@@ -3,7 +3,9 @@
 import datetime
 import logging
 import os
+import random
 import shutil
+import string
 from typing import Any
 
 import cv2
@@ -17,6 +19,8 @@ from frigate.api.auth import require_role
 from frigate.api.defs.request.classification_body import (
     AudioTranscriptionBody,
     DeleteFaceImagesBody,
+    GenerateObjectExamplesBody,
+    GenerateStateExamplesBody,
     RenameFaceBody,
 )
 from frigate.api.defs.response.classification_response import (
@@ -30,6 +34,10 @@ from frigate.config.camera import DetectConfig
 from frigate.const import CLIPS_DIR, FACE_DIR
 from frigate.embeddings import EmbeddingsContext
 from frigate.models import Event
+from frigate.util.classification import (
+    collect_object_classification_examples,
+    collect_state_classification_examples,
+)
 from frigate.util.path import get_event_snapshot
 
 logger = logging.getLogger(__name__)
@@ -159,8 +167,7 @@ def train_face(request: Request, name: str, body: dict = None):
     new_name = f"{sanitized_name}-{datetime.datetime.now().timestamp()}.webp"
     new_file_folder = os.path.join(FACE_DIR, f"{sanitized_name}")
 
-    if not os.path.exists(new_file_folder):
-        os.mkdir(new_file_folder)
+    os.makedirs(new_file_folder, exist_ok=True)
 
     if training_file_name:
         shutil.move(training_file, os.path.join(new_file_folder, new_name))
@@ -701,13 +708,14 @@ def categorize_classification_image(request: Request, name: str, body: dict = No
             status_code=404,
         )
 
-    new_name = f"{category}-{datetime.datetime.now().timestamp()}.png"
+    random_id = "".join(random.choices(string.ascii_lowercase + string.digits, k=6))
+    timestamp = datetime.datetime.now().timestamp()
+    new_name = f"{category}-{timestamp}-{random_id}.png"
     new_file_folder = os.path.join(
         CLIPS_DIR, sanitize_filename(name), "dataset", category
     )
 
-    if not os.path.exists(new_file_folder):
-        os.mkdir(new_file_folder)
+    os.makedirs(new_file_folder, exist_ok=True)
 
     # use opencv because webp images can not be used to train
     img = cv2.imread(training_file)
@@ -756,3 +764,43 @@ def delete_classification_train_images(request: Request, name: str, body: dict =
         content=({"success": True, "message": "Successfully deleted faces."}),
         status_code=200,
     )
+
+
+@router.post(
+    "/classification/generate_examples/state",
+    response_model=GenericResponse,
+    dependencies=[Depends(require_role(["admin"]))],
+    summary="Generate state classification examples",
+)
+async def generate_state_examples(request: Request, body: GenerateStateExamplesBody):
+    """Generate examples for state classification."""
+    model_name = sanitize_filename(body.model_name)
+    cameras_normalized = {
+        camera_name: tuple(crop)
+        for camera_name, crop in body.cameras.items()
+        if camera_name in request.app.frigate_config.cameras
+    }
+
+    collect_state_classification_examples(model_name, cameras_normalized)
+
+    return JSONResponse(
+        content={"success": True, "message": "Example generation completed"},
+        status_code=200,
+    )
+
+
+@router.post(
+    "/classification/generate_examples/object",
+    response_model=GenericResponse,
+    dependencies=[Depends(require_role(["admin"]))],
+    summary="Generate object classification examples",
+)
+async def generate_object_examples(request: Request, body: GenerateObjectExamplesBody):
+    """Generate examples for object classification."""
+    model_name = sanitize_filename(body.model_name)
+    collect_object_classification_examples(model_name, body.label)
+
+    return JSONResponse(
+        content={"success": True, "message": "Example generation completed"},
+        status_code=200,
+    )

frigate/api/defs/request/classification_body.py

@@ -1,17 +1,31 @@
-from typing import List
+from typing import Dict, List, Tuple
 
 from pydantic import BaseModel, Field
 
 
 class RenameFaceBody(BaseModel):
-    new_name: str
+    new_name: str = Field(description="New name for the face")
 
 
 class AudioTranscriptionBody(BaseModel):
-    event_id: str
+    event_id: str = Field(description="ID of the event to transcribe audio for")
 
 
 class DeleteFaceImagesBody(BaseModel):
     ids: List[str] = Field(
         description="List of image filenames to delete from the face folder"
     )
+
+
+class GenerateStateExamplesBody(BaseModel):
+    model_name: str = Field(description="Name of the classification model")
+    cameras: Dict[str, Tuple[float, float, float, float]] = Field(
+        description="Dictionary mapping camera names to normalized crop coordinates in [x1, y1, x2, y2] format (values 0-1)"
+    )
+
+
+class GenerateObjectExamplesBody(BaseModel):
+    model_name: str = Field(description="Name of the classification model")
+    label: str = Field(
+        description="Object label to collect examples for (e.g., 'person', 'car')"
+    )

frigate/api/export.py

@@ -9,6 +9,7 @@ from typing import List
 import psutil
 from fastapi import APIRouter, Depends, Request
 from fastapi.responses import JSONResponse
+from pathvalidate import sanitize_filepath
 from peewee import DoesNotExist
 from playhouse.shortcuts import model_to_dict
 
@@ -26,7 +27,7 @@ from frigate.api.defs.response.export_response import (
 )
 from frigate.api.defs.response.generic_response import GenericResponse
 from frigate.api.defs.tags import Tags
-from frigate.const import EXPORT_DIR
+from frigate.const import CLIPS_DIR, EXPORT_DIR
 from frigate.models import Export, Previews, Recordings
 from frigate.record.export import (
     PlaybackFactorEnum,
@@ -88,7 +89,14 @@ def export_recording(
     playback_factor = body.playback
     playback_source = body.source
     friendly_name = body.name
-    existing_image = body.image_path
+    existing_image = sanitize_filepath(body.image_path) if body.image_path else None
+
+    # Ensure that existing_image is a valid path
+    if existing_image and not existing_image.startswith(CLIPS_DIR):
+        return JSONResponse(
+            content=({"success": False, "message": "Invalid image path"}),
+            status_code=400,
+        )
 
     if playback_source == "recordings":
         recordings_count = (

frigate/api/media.py

@@ -589,7 +589,7 @@ async def no_recordings(
     )
     scale = params.scale
 
-    clauses = [(Recordings.start_time > after) & (Recordings.end_time < before)]
+    clauses = [(Recordings.end_time >= after) & (Recordings.start_time <= before)]
     if cameras != "all":
         camera_list = cameras.split(",")
         clauses.append((Recordings.camera << camera_list))
@@ -608,33 +608,39 @@ async def no_recordings(
     # Convert recordings to list of (start, end) tuples
     recordings = [(r["start_time"], r["end_time"]) for r in data]
 
-    # Generate all time segments
-    current = after
+    # Iterate through time segments and check if each has any recording
     no_recording_segments = []
-    current_start = None
+    current = after
+    current_gap_start = None
 
     while current < before:
-        segment_end = current + scale
-        # Check if segment overlaps with any recording
+        segment_end = min(current + scale, before)
+
+        # Check if this segment overlaps with any recording
         has_recording = any(
-            start <= segment_end and end >= current for start, end in recordings
+            rec_start < segment_end and rec_end > current
+            for rec_start, rec_end in recordings
         )
+
         if not has_recording:
-            if current_start is None:
-                current_start = current  # Start a new gap
+            # This segment has no recordings
+            if current_gap_start is None:
+                current_gap_start = current  # Start a new gap
         else:
-            if current_start is not None:
+            # This segment has recordings
+            if current_gap_start is not None:
                 # End the current gap and append it
                 no_recording_segments.append(
-                    {"start_time": int(current_start), "end_time": int(current)}
+                    {"start_time": int(current_gap_start), "end_time": int(current)}
                 )
-                current_start = None
+                current_gap_start = None
+
         current = segment_end
 
     # Append the last gap if it exists
-    if current_start is not None:
+    if current_gap_start is not None:
         no_recording_segments.append(
-            {"start_time": int(current_start), "end_time": int(before)}
+            {"start_time": int(current_gap_start), "end_time": int(before)}
         )
 
     return JSONResponse(content=no_recording_segments)

frigate/app.py

@@ -488,6 +488,8 @@ class FrigateApp:
                     }
                 ).execute()
 
+                self.config.auth.admin_first_time_login = True
+
                 logger.info("********************************************************")
                 logger.info("********************************************************")
                 logger.info("***    Auth is enabled, but no users exist.          ***")

frigate/config/auth.py

@@ -38,6 +38,13 @@ class AuthConfig(FrigateBaseModel):
         default_factory=dict,
         title="Role to camera mappings. Empty list grants access to all cameras.",
     )
+    admin_first_time_login: Optional[bool] = Field(
+        default=False,
+        title="Internal field to expose first-time admin login flag to the UI",
+        description=(
+            "When true the UI may show a help link on the login page informing users how to sign in after an admin password reset. "
+        ),
+    )
 
     @field_validator("roles")
     @classmethod

frigate/config/camera/birdseye.py

@@ -55,6 +55,12 @@ class BirdseyeConfig(FrigateBaseModel):
     layout: BirdseyeLayoutConfig = Field(
         default_factory=BirdseyeLayoutConfig, title="Birdseye Layout Config"
     )
+    idle_heartbeat_fps: float = Field(
+        default=0.0,
+        ge=0.0,
+        le=10.0,
+        title="Idle heartbeat FPS (0 disables, max 10)",
+    )
 
 
 # uses BaseModel because some global attributes are not available at the camera level

frigate/config/camera/review.py

@@ -1,10 +1,18 @@
+from enum import Enum
 from typing import Optional, Union
 
 from pydantic import Field, field_validator
 
 from ..base import FrigateBaseModel
 
-__all__ = ["ReviewConfig", "DetectionsConfig", "AlertsConfig"]
+__all__ = ["ReviewConfig", "DetectionsConfig", "AlertsConfig", "ImageSourceEnum"]
+
+
+class ImageSourceEnum(str, Enum):
+    """Image source options for GenAI Review."""
+
+    preview = "preview"
+    recordings = "recordings"
 
 
 DEFAULT_ALERT_OBJECTS = ["person", "car"]
@@ -77,6 +85,10 @@ class GenAIReviewConfig(FrigateBaseModel):
     )
     alerts: bool = Field(default=True, title="Enable GenAI for alerts.")
     detections: bool = Field(default=False, title="Enable GenAI for detections.")
+    image_source: ImageSourceEnum = Field(
+        default=ImageSourceEnum.preview,
+        title="Image source for review descriptions.",
+    )
     additional_concerns: list[str] = Field(
         default=[],
         title="Additional concerns that GenAI should make note of on this camera.",
@@ -93,13 +105,40 @@ class GenAIReviewConfig(FrigateBaseModel):
         default=None,
     )
     activity_context_prompt: str = Field(
-        default="""- **Zone context is critical**: Private enclosed spaces (back yards, back decks, fenced areas, inside garages) are resident territory where brief transient activity, routine tasks, and pet care are expected and normal. Front yards, driveways, and porches are semi-public but still resident spaces where deliveries, parking, and coming/going are routine. Consider whether the zone and activity align with normal residential use.
-- **Person + Pet = Normal Activity**: When both "Person" and "Dog" (or "Cat") are detected together in residential zones, this is routine pet care activity (walking, letting out, playing, supervising). Assign Level 0 unless there are OTHER strong suspicious behaviors present (like testing doors, taking items, etc.). A person with their pet in a residential zone is baseline normal activity.
-- Brief appearances in private zones (back yards, garages) are normal residential patterns.
-- Normal residential activity includes: residents, family members, guests, deliveries, services, maintenance workers, routine property use (parking, unloading, mail pickup, trash removal).
-- Brief movement with legitimate items (bags, packages, tools, equipment) in appropriate zones is routine.
-""",
-        title="Custom activity context prompt defining normal activity patterns for this property.",
+        default="""### Normal Activity Indicators (Level 0)
+- Known/verified people in any zone at any time
+- People with pets in residential areas
+- Deliveries or services during daytime/evening (6 AM - 10 PM): carrying packages to doors/porches, placing items, leaving
+- Services/maintenance workers with visible tools, uniforms, or service vehicles during daytime
+- Activity confined to public areas only (sidewalks, streets) without entering property at any time
+
+### Suspicious Activity Indicators (Level 1)
+- **Testing or attempting to open doors/windows/handles on vehicles or buildings** — ALWAYS Level 1 regardless of time or duration
+- **Unidentified person in private areas (driveways, near vehicles/buildings) during late night/early morning (11 PM - 5 AM)** — ALWAYS Level 1 regardless of activity or duration
+- Taking items that don't belong to them (packages, objects from porches/driveways)
+- Climbing or jumping fences/barriers to access property
+- Attempting to conceal actions or items from view
+- Prolonged loitering: remaining in same area without visible purpose throughout most of the sequence
+
+### Critical Threat Indicators (Level 2)
+- Holding break-in tools (crowbars, pry bars, bolt cutters)
+- Weapons visible (guns, knives, bats used aggressively)
+- Forced entry in progress
+- Physical aggression or violence
+- Active property damage or theft in progress
+
+### Assessment Guidance
+Evaluate in this order:
+
+1. **If person is verified/known** → Level 0 regardless of time or activity
+2. **If person is unidentified:**
+   - Check time: If late night/early morning (11 PM - 5 AM) AND in private areas (driveways, near vehicles/buildings) → Level 1
+   - Check actions: If testing doors/handles, taking items, climbing → Level 1
+   - Otherwise, if daytime/evening (6 AM - 10 PM) with clear legitimate purpose (delivery, service worker) → Level 0
+3. **Escalate to Level 2 if:** Weapons, break-in tools, forced entry in progress, violence, or active property damage visible (escalates from Level 0 or 1)
+
+The mere presence of an unidentified person in private areas during late night hours is inherently suspicious and warrants human review, regardless of what activity they appear to be doing or how brief the sequence is.""",
+        title="Custom activity context prompt defining normal and suspicious activity patterns for this property.",
     )
 
 

frigate/config/classification.py

@@ -33,6 +33,8 @@ class TriggerType(str, Enum):
 
 class TriggerAction(str, Enum):
     NOTIFICATION = "notification"
+    SUB_LABEL = "sub_label"
+    ATTRIBUTE = "attribute"
 
 
 class ObjectClassificationType(str, Enum):
@@ -69,7 +71,7 @@ class BirdClassificationConfig(FrigateBaseModel):
 
 
 class CustomClassificationStateCameraConfig(FrigateBaseModel):
-    crop: list[int, int, int, int] = Field(
+    crop: list[float, float, float, float] = Field(
         title="Crop of image frame on this camera to run classification on."
     )
 
@@ -197,7 +199,9 @@ class FaceRecognitionConfig(FrigateBaseModel):
         title="Min face recognitions for the sub label to be applied to the person object.",
     )
     save_attempts: int = Field(
-        default=100, ge=0, title="Number of face attempts to save in the train tab."
+        default=200,
+        ge=0,
+        title="Number of face attempts to save in the recent recognitions tab.",
     )
     blur_confidence_filter: bool = Field(
         default=True, title="Apply blur quality filter to face confidence."

frigate/data_processing/post/review_descriptions.py

@@ -3,6 +3,7 @@
 import copy
 import datetime
 import logging
+import math
 import os
 import shutil
 import threading
@@ -10,22 +11,26 @@ from pathlib import Path
 from typing import Any
 
 import cv2
+from peewee import DoesNotExist
 
 from frigate.comms.embeddings_updater import EmbeddingsRequestEnum
 from frigate.comms.inter_process import InterProcessRequestor
 from frigate.config import FrigateConfig
-from frigate.config.camera.review import GenAIReviewConfig
+from frigate.config.camera.review import GenAIReviewConfig, ImageSourceEnum
 from frigate.const import CACHE_DIR, CLIPS_DIR, UPDATE_REVIEW_DESCRIPTION
 from frigate.data_processing.types import PostProcessDataEnum
 from frigate.genai import GenAIClient
-from frigate.models import ReviewSegment
+from frigate.models import Recordings, ReviewSegment
 from frigate.util.builtin import EventsPerSecond, InferenceSpeed
+from frigate.util.image import get_image_from_recording
 
 from ..post.api import PostProcessorApi
 from ..types import DataProcessorMetrics
 
 logger = logging.getLogger(__name__)
 
+RECORDING_BUFFER_EXTENSION_PERCENT = 0.10
+
 
 class ReviewDescriptionProcessor(PostProcessorApi):
     def __init__(
@@ -43,20 +48,52 @@ class ReviewDescriptionProcessor(PostProcessorApi):
         self.review_descs_dps = EventsPerSecond()
         self.review_descs_dps.start()
 
-    def calculate_frame_count(self) -> int:
-        """Calculate optimal number of frames based on context size."""
-        # With our preview images (height of 180px) each image should be ~100 tokens per image
-        # We want to be conservative to not have too long of query times with too many images
-        context_size = self.genai_client.get_context_size()
+    def calculate_frame_count(
+        self,
+        camera: str,
+        image_source: ImageSourceEnum = ImageSourceEnum.preview,
+        height: int = 480,
+    ) -> int:
+        """Calculate optimal number of frames based on context size, image source, and resolution.
 
-        if context_size > 10000:
-            return 20
-        elif context_size > 6000:
-            return 16
-        elif context_size > 4000:
-            return 12
+        Token usage varies by resolution: larger images (ultrawide aspect ratios) use more tokens.
+        Estimates ~1 token per 1250 pixels. Targets 98% context utilization with safety margin.
+        Capped at 20 frames.
+        """
+        context_size = self.genai_client.get_context_size()
+        camera_config = self.config.cameras[camera]
+
+        detect_width = camera_config.detect.width
+        detect_height = camera_config.detect.height
+        aspect_ratio = detect_width / detect_height
+
+        if image_source == ImageSourceEnum.recordings:
+            if aspect_ratio >= 1:
+                # Landscape or square: constrain height
+                width = int(height * aspect_ratio)
+            else:
+                # Portrait: constrain width
+                width = height
+                height = int(width / aspect_ratio)
         else:
-            return 8
+            if aspect_ratio >= 1:
+                # Landscape or square: constrain height
+                target_height = 180
+                width = int(target_height * aspect_ratio)
+                height = target_height
+            else:
+                # Portrait: constrain width
+                target_width = 180
+                width = target_width
+                height = int(target_width / aspect_ratio)
+
+        pixels_per_image = width * height
+        tokens_per_image = pixels_per_image / 1250
+        prompt_tokens = 3500
+        available_tokens = context_size * 0.98 - prompt_tokens
+        max_frames = int(available_tokens / tokens_per_image)
+
+        return min(max(max_frames, 3), 20)
 
     def process_data(self, data, data_type):
         self.metrics.review_desc_dps.value = self.review_descs_dps.eps()
@@ -88,36 +125,53 @@ class ReviewDescriptionProcessor(PostProcessorApi):
             ):
                 return
 
-            frames = self.get_cache_frames(
-                camera, final_data["start_time"], final_data["end_time"]
-            )
+            image_source = camera_config.review.genai.image_source
 
-            if not frames:
-                frames = [final_data["thumb_path"]]
+            if image_source == ImageSourceEnum.recordings:
+                duration = final_data["end_time"] - final_data["start_time"]
+                buffer_extension = duration * RECORDING_BUFFER_EXTENSION_PERCENT
 
-            thumbs = []
-
-            for idx, thumb_path in enumerate(frames):
-                thumb_data = cv2.imread(thumb_path)
-                ret, jpg = cv2.imencode(
-                    ".jpg", thumb_data, [int(cv2.IMWRITE_JPEG_QUALITY), 100]
+                thumbs = self.get_recording_frames(
+                    camera,
+                    final_data["start_time"] - buffer_extension,
+                    final_data["end_time"] + buffer_extension,
+                    height=480,  # Use 480p for good balance between quality and token usage
                 )
 
-                if ret:
-                    thumbs.append(jpg.tobytes())
-
-                if camera_config.review.genai.debug_save_thumbnails:
-                    id = data["after"]["id"]
-                    Path(os.path.join(CLIPS_DIR, "genai-requests", f"{id}")).mkdir(
+                if not thumbs:
+                    # Fallback to preview frames if no recordings available
+                    logger.warning(
+                        f"No recording frames found for {camera}, falling back to preview frames"
+                    )
+                    thumbs = self.get_preview_frames_as_bytes(
+                        camera,
+                        final_data["start_time"],
+                        final_data["end_time"],
+                        final_data["thumb_path"],
+                        id,
+                        camera_config.review.genai.debug_save_thumbnails,
+                    )
+                elif camera_config.review.genai.debug_save_thumbnails:
+                    # Save debug thumbnails for recordings
+                    Path(os.path.join(CLIPS_DIR, "genai-requests", id)).mkdir(
                         parents=True, exist_ok=True
                     )
-                    shutil.copy(
-                        thumb_path,
-                        os.path.join(
-                            CLIPS_DIR,
-                            f"genai-requests/{id}/{idx}.webp",
-                        ),
-                    )
+                    for idx, frame_bytes in enumerate(thumbs):
+                        with open(
+                            os.path.join(CLIPS_DIR, f"genai-requests/{id}/{idx}.jpg"),
+                            "wb",
+                        ) as f:
+                            f.write(frame_bytes)
+            else:
+                # Use preview frames
+                thumbs = self.get_preview_frames_as_bytes(
+                    camera,
+                    final_data["start_time"],
+                    final_data["end_time"],
+                    final_data["thumb_path"],
+                    id,
+                    camera_config.review.genai.debug_save_thumbnails,
+                )
 
             # kickoff analysis
             self.review_descs_dps.update()
@@ -132,6 +186,7 @@ class ReviewDescriptionProcessor(PostProcessorApi):
                     thumbs,
                     camera_config.review.genai,
                     list(self.config.model.merged_labelmap.values()),
+                    self.config.model.all_attributes,
                 ),
             ).start()
 
@@ -217,7 +272,7 @@ class ReviewDescriptionProcessor(PostProcessorApi):
             all_frames.append(os.path.join(preview_dir, file))
 
         frame_count = len(all_frames)
-        desired_frame_count = self.calculate_frame_count()
+        desired_frame_count = self.calculate_frame_count(camera)
 
         if frame_count <= desired_frame_count:
             return all_frames
@@ -231,6 +286,124 @@ class ReviewDescriptionProcessor(PostProcessorApi):
 
         return selected_frames
 
+    def get_recording_frames(
+        self,
+        camera: str,
+        start_time: float,
+        end_time: float,
+        height: int = 480,
+    ) -> list[bytes]:
+        """Get frames from recordings at specified timestamps."""
+        duration = end_time - start_time
+        desired_frame_count = self.calculate_frame_count(
+            camera, ImageSourceEnum.recordings, height
+        )
+
+        # Calculate evenly spaced timestamps throughout the duration
+        if desired_frame_count == 1:
+            timestamps = [start_time + duration / 2]
+        else:
+            step = duration / (desired_frame_count - 1)
+            timestamps = [start_time + (i * step) for i in range(desired_frame_count)]
+
+        def extract_frame_from_recording(ts: float) -> bytes | None:
+            """Extract a single frame from recording at given timestamp."""
+            try:
+                recording = (
+                    Recordings.select(
+                        Recordings.path,
+                        Recordings.start_time,
+                    )
+                    .where((ts >= Recordings.start_time) & (ts <= Recordings.end_time))
+                    .where(Recordings.camera == camera)
+                    .order_by(Recordings.start_time.desc())
+                    .limit(1)
+                    .get()
+                )
+
+                time_in_segment = ts - recording.start_time
+                return get_image_from_recording(
+                    self.config.ffmpeg,
+                    recording.path,
+                    time_in_segment,
+                    "mjpeg",
+                    height=height,
+                )
+            except DoesNotExist:
+                return None
+
+        frames = []
+
+        for timestamp in timestamps:
+            try:
+                # Try to extract frame at exact timestamp
+                image_data = extract_frame_from_recording(timestamp)
+
+                if not image_data:
+                    # Try with rounded timestamp as fallback
+                    rounded_timestamp = math.ceil(timestamp)
+                    image_data = extract_frame_from_recording(rounded_timestamp)
+
+                if image_data:
+                    frames.append(image_data)
+                else:
+                    logger.warning(
+                        f"No recording found for {camera} at timestamp {timestamp}"
+                    )
+            except Exception as e:
+                logger.error(
+                    f"Error extracting frame from recording for {camera} at {timestamp}: {e}"
+                )
+                continue
+
+        return frames
+
+    def get_preview_frames_as_bytes(
+        self,
+        camera: str,
+        start_time: float,
+        end_time: float,
+        thumb_path_fallback: str,
+        review_id: str,
+        save_debug: bool,
+    ) -> list[bytes]:
+        """Get preview frames and convert them to JPEG bytes.
+
+        Args:
+            camera: Camera name
+            start_time: Start timestamp
+            end_time: End timestamp
+            thumb_path_fallback: Fallback thumbnail path if no preview frames found
+            review_id: Review item ID for debug saving
+            save_debug: Whether to save debug thumbnails
+
+        Returns:
+            List of JPEG image bytes
+        """
+        frame_paths = self.get_cache_frames(camera, start_time, end_time)
+        if not frame_paths:
+            frame_paths = [thumb_path_fallback]
+
+        thumbs = []
+        for idx, thumb_path in enumerate(frame_paths):
+            thumb_data = cv2.imread(thumb_path)
+            ret, jpg = cv2.imencode(
+                ".jpg", thumb_data, [int(cv2.IMWRITE_JPEG_QUALITY), 100]
+            )
+            if ret:
+                thumbs.append(jpg.tobytes())
+
+            if save_debug:
+                Path(os.path.join(CLIPS_DIR, "genai-requests", review_id)).mkdir(
+                    parents=True, exist_ok=True
+                )
+                shutil.copy(
+                    thumb_path,
+                    os.path.join(CLIPS_DIR, f"genai-requests/{review_id}/{idx}.webp"),
+                )
+
+        return thumbs
+
 
 @staticmethod
 def run_analysis(
@@ -242,6 +415,7 @@ def run_analysis(
     thumbs: list[bytes],
     genai_config: GenAIReviewConfig,
     labelmap_objects: list[str],
+    attribute_labels: list[str],
 ) -> None:
     start = datetime.datetime.now().timestamp()
     analytics_data = {
@@ -254,25 +428,28 @@ def run_analysis(
         "duration": round(final_data["end_time"] - final_data["start_time"]),
     }
 
-    objects = []
-    named_objects = []
+    unified_objects = []
 
     objects_list = final_data["data"]["objects"]
     sub_labels_list = final_data["data"]["sub_labels"]
 
+    for i, verified_label in enumerate(final_data["data"]["verified_objects"]):
+        object_type = verified_label.replace("-verified", "").replace("_", " ")
+        name = sub_labels_list[i].replace("_", " ").title()
+        unified_objects.append(f"{name} ({object_type})")
+
     for label in objects_list:
         if "-verified" in label:
             continue
         elif label in labelmap_objects:
-            objects.append(label.replace("_", " ").title())
+            object_type = label.replace("_", " ").title()
 
-    for i, verified_label in enumerate(final_data["data"]["verified_objects"]):
-        named_objects.append(
-            f"{sub_labels_list[i].replace('_', ' ').title()} ({verified_label.replace('-verified', '')})"
-        )
+            if label in attribute_labels:
+                unified_objects.append(f"{object_type} (delivery/service)")
+            else:
+                unified_objects.append(object_type)
 
-    analytics_data["objects"] = objects
-    analytics_data["recognized_objects"] = named_objects
+    analytics_data["unified_objects"] = unified_objects
 
     metadata = genai_client.generate_review_description(
         analytics_data,

frigate/data_processing/post/semantic_trigger.py

@@ -10,6 +10,10 @@ import cv2
 import numpy as np
 from peewee import DoesNotExist
 
+from frigate.comms.event_metadata_updater import (
+    EventMetadataPublisher,
+    EventMetadataTypeEnum,
+)
 from frigate.comms.inter_process import InterProcessRequestor
 from frigate.config import FrigateConfig
 from frigate.const import CONFIG_DIR
@@ -34,6 +38,7 @@ class SemanticTriggerProcessor(PostProcessorApi):
         db: SqliteVecQueueDatabase,
         config: FrigateConfig,
         requestor: InterProcessRequestor,
+        sub_label_publisher: EventMetadataPublisher,
         metrics: DataProcessorMetrics,
         embeddings,
     ):
@@ -41,6 +46,7 @@ class SemanticTriggerProcessor(PostProcessorApi):
         self.db = db
         self.embeddings = embeddings
         self.requestor = requestor
+        self.sub_label_publisher = sub_label_publisher
         self.trigger_embeddings: list[np.ndarray] = []
 
         self.thumb_stats = ZScoreNormalization()
@@ -184,14 +190,44 @@ class SemanticTriggerProcessor(PostProcessorApi):
                     ),
                 )
 
+                friendly_name = (
+                    self.config.cameras[camera]
+                    .semantic_search.triggers[trigger["name"]]
+                    .friendly_name
+                )
+
                 if (
                     self.config.cameras[camera]
                     .semantic_search.triggers[trigger["name"]]
                     .actions
                 ):
-                    # TODO: handle actions for the trigger
+                    # handle actions for the trigger
                     # notifications already handled by webpush
-                    pass
+                    if (
+                        "sub_label"
+                        in self.config.cameras[camera]
+                        .semantic_search.triggers[trigger["name"]]
+                        .actions
+                    ):
+                        self.sub_label_publisher.publish(
+                            (event_id, friendly_name, similarity),
+                            EventMetadataTypeEnum.sub_label,
+                        )
+                    if (
+                        "attribute"
+                        in self.config.cameras[camera]
+                        .semantic_search.triggers[trigger["name"]]
+                        .actions
+                    ):
+                        self.sub_label_publisher.publish(
+                            (
+                                event_id,
+                                trigger["name"],
+                                trigger["type"],
+                                similarity,
+                            ),
+                            EventMetadataTypeEnum.attribute.value,
+                        )
 
             if WRITE_DEBUG_IMAGES:
                 try:

frigate/data_processing/real_time/custom_classification.py

@@ -34,6 +34,8 @@ except ModuleNotFoundError:
 
 logger = logging.getLogger(__name__)
 
+MAX_OBJECT_CLASSIFICATIONS = 16
+
 
 class CustomStateClassificationProcessor(RealTimeProcessorApi):
     def __init__(
@@ -53,9 +55,18 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
         self.tensor_output_details: dict[str, Any] | None = None
         self.labelmap: dict[int, str] = {}
         self.classifications_per_second = EventsPerSecond()
-        self.inference_speed = InferenceSpeed(
-            self.metrics.classification_speeds[self.model_config.name]
-        )
+        self.state_history: dict[str, dict[str, Any]] = {}
+
+        if (
+            self.metrics
+            and self.model_config.name in self.metrics.classification_speeds
+        ):
+            self.inference_speed = InferenceSpeed(
+                self.metrics.classification_speeds[self.model_config.name]
+            )
+        else:
+            self.inference_speed = None
+
         self.last_run = datetime.datetime.now().timestamp()
         self.__build_detector()
 
@@ -83,12 +94,50 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
 
     def __update_metrics(self, duration: float) -> None:
         self.classifications_per_second.update()
-        self.inference_speed.update(duration)
+        if self.inference_speed:
+            self.inference_speed.update(duration)
+
+    def verify_state_change(self, camera: str, detected_state: str) -> str | None:
+        """
+        Verify state change requires 3 consecutive identical states before publishing.
+        Returns state to publish or None if verification not complete.
+        """
+        if camera not in self.state_history:
+            self.state_history[camera] = {
+                "current_state": None,
+                "pending_state": None,
+                "consecutive_count": 0,
+            }
+
+        verification = self.state_history[camera]
+
+        if detected_state == verification["current_state"]:
+            verification["pending_state"] = None
+            verification["consecutive_count"] = 0
+            return None
+
+        if detected_state == verification["pending_state"]:
+            verification["consecutive_count"] += 1
+
+            if verification["consecutive_count"] >= 3:
+                verification["current_state"] = detected_state
+                verification["pending_state"] = None
+                verification["consecutive_count"] = 0
+                return detected_state
+        else:
+            verification["pending_state"] = detected_state
+            verification["consecutive_count"] = 1
+            logger.debug(
+                f"New state '{detected_state}' detected for {camera}, need {3 - verification['consecutive_count']} more consecutive detections"
+            )
+
+        return None
 
     def process_frame(self, frame_data: dict[str, Any], frame: np.ndarray):
-        self.metrics.classification_cps[
-            self.model_config.name
-        ].value = self.classifications_per_second.eps()
+        if self.metrics and self.model_config.name in self.metrics.classification_cps:
+            self.metrics.classification_cps[
+                self.model_config.name
+            ].value = self.classifications_per_second.eps()
         camera = frame_data.get("camera")
 
         if camera not in self.model_config.state_config.cameras:
@@ -96,10 +145,10 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
 
         camera_config = self.model_config.state_config.cameras[camera]
         crop = [
-            camera_config.crop[0],
-            camera_config.crop[1],
-            camera_config.crop[2],
-            camera_config.crop[3],
+            camera_config.crop[0] * self.config.cameras[camera].detect.width,
+            camera_config.crop[1] * self.config.cameras[camera].detect.height,
+            camera_config.crop[2] * self.config.cameras[camera].detect.width,
+            camera_config.crop[3] * self.config.cameras[camera].detect.height,
         ]
         should_run = False
 
@@ -121,6 +170,19 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
                 self.last_run = now
                 should_run = True
 
+        # Shortcut: always run if we have a pending state verification to complete
+        if (
+            not should_run
+            and camera in self.state_history
+            and self.state_history[camera]["pending_state"] is not None
+            and now > self.last_run + 0.5
+        ):
+            self.last_run = now
+            should_run = True
+            logger.debug(
+                f"Running verification check for pending state: {self.state_history[camera]['pending_state']} ({self.state_history[camera]['consecutive_count']}/3)"
+            )
+
         if not should_run:
             return
 
@@ -178,10 +240,19 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
             score,
         )
 
-        if score >= self.model_config.threshold:
+        if score < self.model_config.threshold:
+            logger.debug(
+                f"Score {score} below threshold {self.model_config.threshold}, skipping verification"
+            )
+            return
+
+        detected_state = self.labelmap[best_id]
+        verified_state = self.verify_state_change(camera, detected_state)
+
+        if verified_state is not None:
             self.requestor.send_data(
                 f"{camera}/classification/{self.model_config.name}",
-                self.labelmap[best_id],
+                verified_state,
             )
 
     def handle_request(self, topic, request_data):
@@ -220,12 +291,20 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
         self.sub_label_publisher = sub_label_publisher
         self.tensor_input_details: dict[str, Any] | None = None
         self.tensor_output_details: dict[str, Any] | None = None
-        self.detected_objects: dict[str, float] = {}
+        self.classification_history: dict[str, list[tuple[str, float, float]]] = {}
         self.labelmap: dict[int, str] = {}
         self.classifications_per_second = EventsPerSecond()
-        self.inference_speed = InferenceSpeed(
-            self.metrics.classification_speeds[self.model_config.name]
-        )
+
+        if (
+            self.metrics
+            and self.model_config.name in self.metrics.classification_speeds
+        ):
+            self.inference_speed = InferenceSpeed(
+                self.metrics.classification_speeds[self.model_config.name]
+            )
+        else:
+            self.inference_speed = None
+
         self.__build_detector()
 
     @redirect_output_to_logger(logger, logging.DEBUG)
@@ -251,12 +330,64 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
 
     def __update_metrics(self, duration: float) -> None:
         self.classifications_per_second.update()
-        self.inference_speed.update(duration)
+        if self.inference_speed:
+            self.inference_speed.update(duration)
+
+    def get_weighted_score(
+        self,
+        object_id: str,
+        current_label: str,
+        current_score: float,
+        current_time: float,
+    ) -> tuple[str | None, float]:
+        """
+        Determine weighted score based on history to prevent false positives/negatives.
+        Requires 60% of attempts to agree on a label before publishing.
+        Returns (weighted_label, weighted_score) or (None, 0.0) if no weighted score.
+        """
+        if object_id not in self.classification_history:
+            self.classification_history[object_id] = []
+
+        self.classification_history[object_id].append(
+            (current_label, current_score, current_time)
+        )
+
+        history = self.classification_history[object_id]
+
+        if len(history) < 3:
+            return None, 0.0
+
+        label_counts = {}
+        label_scores = {}
+        total_attempts = len(history)
+
+        for label, score, timestamp in history:
+            if label not in label_counts:
+                label_counts[label] = 0
+                label_scores[label] = []
+
+            label_counts[label] += 1
+            label_scores[label].append(score)
+
+        best_label = max(label_counts, key=label_counts.get)
+        best_count = label_counts[best_label]
+
+        consensus_threshold = total_attempts * 0.6
+        if best_count < consensus_threshold:
+            return None, 0.0
+
+        avg_score = sum(label_scores[best_label]) / len(label_scores[best_label])
+
+        if best_label == "none":
+            return None, 0.0
+
+        return best_label, avg_score
 
     def process_frame(self, obj_data, frame):
-        self.metrics.classification_cps[
-            self.model_config.name
-        ].value = self.classifications_per_second.eps()
+        if self.metrics and self.model_config.name in self.metrics.classification_cps:
+            self.metrics.classification_cps[
+                self.model_config.name
+            ].value = self.classifications_per_second.eps()
 
         if obj_data["false_positive"]:
             return
@@ -264,6 +395,21 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
         if obj_data["label"] not in self.model_config.object_config.objects:
             return
 
+        if obj_data.get("end_time") is not None:
+            return
+
+        if obj_data.get("stationary"):
+            return
+
+        object_id = obj_data["id"]
+
+        if (
+            object_id in self.classification_history
+            and len(self.classification_history[object_id])
+            >= MAX_OBJECT_CLASSIFICATIONS
+        ):
+            return
+
         now = datetime.datetime.now().timestamp()
         x, y, x2, y2 = calculate_region(
             frame.shape,
@@ -295,7 +441,7 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
             write_classification_attempt(
                 self.train_dir,
                 cv2.cvtColor(crop, cv2.COLOR_RGB2BGR),
-                obj_data["id"],
+                object_id,
                 now,
                 "unknown",
                 0.0,
@@ -311,13 +457,12 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
         probs = res / res.sum(axis=0)
         best_id = np.argmax(probs)
         score = round(probs[best_id], 2)
-        previous_score = self.detected_objects.get(obj_data["id"], 0.0)
         self.__update_metrics(datetime.datetime.now().timestamp() - now)
 
         write_classification_attempt(
             self.train_dir,
             cv2.cvtColor(crop, cv2.COLOR_RGB2BGR),
-            obj_data["id"],
+            object_id,
             now,
             self.labelmap[best_id],
             score,
@@ -327,30 +472,34 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
             logger.debug(f"Score {score} is less than threshold.")
             return
 
-        if score <= previous_score:
-            logger.debug(f"Score {score} is worse than previous score {previous_score}")
-            return
-
         sub_label = self.labelmap[best_id]
-        self.detected_objects[obj_data["id"]] = score
 
-        if (
-            self.model_config.object_config.classification_type
-            == ObjectClassificationType.sub_label
-        ):
-            if sub_label != "none":
+        consensus_label, consensus_score = self.get_weighted_score(
+            object_id, sub_label, score, now
+        )
+
+        if consensus_label is not None:
+            if (
+                self.model_config.object_config.classification_type
+                == ObjectClassificationType.sub_label
+            ):
                 self.sub_label_publisher.publish(
-                    (obj_data["id"], sub_label, score),
+                    (object_id, consensus_label, consensus_score),
                     EventMetadataTypeEnum.sub_label,
                 )
-        elif (
-            self.model_config.object_config.classification_type
-            == ObjectClassificationType.attribute
-        ):
-            self.sub_label_publisher.publish(
-                (obj_data["id"], self.model_config.name, sub_label, score),
-                EventMetadataTypeEnum.attribute.value,
-            )
+            elif (
+                self.model_config.object_config.classification_type
+                == ObjectClassificationType.attribute
+            ):
+                self.sub_label_publisher.publish(
+                    (
+                        object_id,
+                        self.model_config.name,
+                        consensus_label,
+                        consensus_score,
+                    ),
+                    EventMetadataTypeEnum.attribute.value,
+                )
 
     def handle_request(self, topic, request_data):
         if topic == EmbeddingsRequestEnum.reload_classification_model.value:
@@ -368,8 +517,8 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
             return None
 
     def expire_object(self, object_id, camera):
-        if object_id in self.detected_objects:
-            self.detected_objects.pop(object_id)
+        if object_id in self.classification_history:
+            self.classification_history.pop(object_id)
 
 
 @staticmethod

frigate/detectors/detection_runners.py

@@ -21,21 +21,25 @@ def is_arm64_platform() -> bool:
     return machine in ("aarch64", "arm64", "armv8", "armv7l")
 
 
-def get_ort_session_options() -> ort.SessionOptions | None:
+def get_ort_session_options(
+    is_complex_model: bool = False,
+) -> ort.SessionOptions | None:
     """Get ONNX Runtime session options with appropriate settings.
 
-    On ARM/RKNN platforms, use basic optimizations to avoid graph fusion issues
-    that can break certain models. On amd64, use default optimizations for better performance.
-    """
-    sess_options = None
+    Args:
+        is_complex_model: Whether the model needs basic optimization to avoid graph fusion issues.
 
-    if is_arm64_platform():
+    Returns:
+        SessionOptions with appropriate optimization level, or None for default settings.
+    """
+    if is_complex_model:
         sess_options = ort.SessionOptions()
         sess_options.graph_optimization_level = (
             ort.GraphOptimizationLevel.ORT_ENABLE_BASIC
         )
+        return sess_options
 
-    return sess_options
+    return None
 
 
 # Import OpenVINO only when needed to avoid circular dependencies
@@ -103,6 +107,21 @@ 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
@@ -312,11 +331,13 @@ class OpenVINOModelRunner(BaseModelRunner):
 
             # Multiple inputs case - set each input by name
             for input_name, input_data in inputs.items():
-                # Find the input by name
+                # Find the input by name and its index
                 input_port = None
-                for port in self.compiled_model.inputs:
+                input_index = None
+                for idx, port in enumerate(self.compiled_model.inputs):
                     if port.get_any_name() == input_name:
                         input_port = port
+                        input_index = idx
                         break
 
                 if input_port is None:
@@ -327,8 +348,8 @@ class OpenVINOModelRunner(BaseModelRunner):
                 input_tensor = ov.Tensor(input_element_type, input_data.shape)
                 np.copyto(input_tensor.data, input_data)
 
-                # Set the input tensor
-                self.infer_request.set_input_tensor(input_tensor)
+                # Set the input tensor for the specific port index
+                self.infer_request.set_input_tensor(input_index, input_tensor)
 
             # Run inference
             self.infer_request.infer()
@@ -494,7 +515,9 @@ def get_optimized_runner(
     return ONNXModelRunner(
         ort.InferenceSession(
             model_path,
-            sess_options=get_ort_session_options(),
+            sess_options=get_ort_session_options(
+                ONNXModelRunner.is_cpu_complex_model(model_type)
+            ),
             providers=providers,
             provider_options=options,
         )

frigate/embeddings/maintainer.py

@@ -9,6 +9,7 @@ from typing import Any
 
 from peewee import DoesNotExist
 
+from frigate.comms.config_updater import ConfigSubscriber
 from frigate.comms.detections_updater import DetectionSubscriber, DetectionTypeEnum
 from frigate.comms.embeddings_updater import (
     EmbeddingsRequestEnum,
@@ -95,6 +96,9 @@ class EmbeddingMaintainer(threading.Thread):
                 CameraConfigUpdateEnum.semantic_search,
             ],
         )
+        self.classification_config_subscriber = ConfigSubscriber(
+            "config/classification/custom/"
+        )
 
         # Configure Frigate DB
         db = SqliteVecQueueDatabase(
@@ -229,6 +233,7 @@ class EmbeddingMaintainer(threading.Thread):
                 db,
                 self.config,
                 self.requestor,
+                self.event_metadata_publisher,
                 metrics,
                 self.embeddings,
             )
@@ -255,6 +260,7 @@ class EmbeddingMaintainer(threading.Thread):
         """Maintain a SQLite-vec database for semantic search."""
         while not self.stop_event.is_set():
             self.config_updater.check_for_updates()
+            self._check_classification_config_updates()
             self._process_requests()
             self._process_updates()
             self._process_recordings_updates()
@@ -265,6 +271,7 @@ class EmbeddingMaintainer(threading.Thread):
             self._process_event_metadata()
 
         self.config_updater.stop()
+        self.classification_config_subscriber.stop()
         self.event_subscriber.stop()
         self.event_end_subscriber.stop()
         self.recordings_subscriber.stop()
@@ -275,6 +282,46 @@ class EmbeddingMaintainer(threading.Thread):
         self.requestor.stop()
         logger.info("Exiting embeddings maintenance...")
 
+    def _check_classification_config_updates(self) -> None:
+        """Check for classification config updates and add new processors."""
+        topic, model_config = self.classification_config_subscriber.check_for_update()
+
+        if topic and model_config:
+            model_name = topic.split("/")[-1]
+            self.config.classification.custom[model_name] = model_config
+
+            # Check if processor already exists
+            for processor in self.realtime_processors:
+                if isinstance(
+                    processor,
+                    (
+                        CustomStateClassificationProcessor,
+                        CustomObjectClassificationProcessor,
+                    ),
+                ):
+                    if processor.model_config.name == model_name:
+                        logger.debug(
+                            f"Classification processor for model {model_name} already exists, skipping"
+                        )
+                        return
+
+            if model_config.state_config is not None:
+                processor = CustomStateClassificationProcessor(
+                    self.config, model_config, self.requestor, self.metrics
+                )
+            else:
+                processor = CustomObjectClassificationProcessor(
+                    self.config,
+                    model_config,
+                    self.event_metadata_publisher,
+                    self.metrics,
+                )
+
+            self.realtime_processors.append(processor)
+            logger.info(
+                f"Added classification processor for model: {model_name} (type: {type(processor).__name__})"
+            )
+
     def _process_requests(self) -> None:
         """Process embeddings requests"""
 

frigate/ffmpeg_presets.py

@@ -150,10 +150,10 @@ PRESETS_HW_ACCEL_SCALE["preset-rk-h265"] = PRESETS_HW_ACCEL_SCALE[FFMPEG_HWACCEL
 PRESETS_HW_ACCEL_ENCODE_BIRDSEYE = {
     "preset-rpi-64-h264": "{0} -hide_banner {1} -c:v h264_v4l2m2m {2}",
     "preset-rpi-64-h265": "{0} -hide_banner {1} -c:v hevc_v4l2m2m {2}",
-    FFMPEG_HWACCEL_VAAPI: "{0} -hide_banner -hwaccel vaapi -hwaccel_output_format vaapi {3} {1} -c:v h264_vaapi -g 50 -bf 0 -profile:v high -level:v 4.1 -sei:v 0 -an -vf format=vaapi|nv12,hwupload {2}",
+    FFMPEG_HWACCEL_VAAPI: "{0} -hide_banner -hwaccel vaapi -hwaccel_output_format vaapi -hwaccel_device {3} {1} -c:v h264_vaapi -g 50 -bf 0 -profile:v high -level:v 4.1 -sei:v 0 -an -vf format=vaapi|nv12,hwupload {2}",
     "preset-intel-qsv-h264": "{0} -hide_banner {1} -c:v h264_qsv -g 50 -bf 0 -profile:v high -level:v 4.1 -async_depth:v 1 {2}",
     "preset-intel-qsv-h265": "{0} -hide_banner {1} -c:v h264_qsv -g 50 -bf 0 -profile:v main -level:v 4.1 -async_depth:v 1 {2}",
-    FFMPEG_HWACCEL_NVIDIA: "{0} -hide_banner {1} {3} -c:v h264_nvenc -g 50 -profile:v high -level:v auto -preset:v p2 -tune:v ll {2}",
+    FFMPEG_HWACCEL_NVIDIA: "{0} -hide_banner {1} -hwaccel device {3} -c:v h264_nvenc -g 50 -profile:v high -level:v auto -preset:v p2 -tune:v ll {2}",
     "preset-jetson-h264": "{0} -hide_banner {1} -c:v h264_nvmpi -profile high {2}",
     "preset-jetson-h265": "{0} -hide_banner {1} -c:v h264_nvmpi -profile main {2}",
     FFMPEG_HWACCEL_RKMPP: "{0} -hide_banner {1} -c:v h264_rkmpp -profile:v high {2}",
@@ -246,7 +246,7 @@ def parse_preset_hardware_acceleration_scale(
         ",hwdownload,format=nv12,eq=gamma=1.4:gamma_weight=0.5" in scale
         and os.environ.get("FFMPEG_DISABLE_GAMMA_EQUALIZER") is not None
     ):
-        scale.replace(
+        scale = scale.replace(
             ",hwdownload,format=nv12,eq=gamma=1.4:gamma_weight=0.5",
             ":format=nv12,hwdownload,format=nv12,format=yuv420p",
         )

frigate/genai/__init__.py

@@ -63,58 +63,68 @@ class GenAIClient:
             else:
                 return ""
 
-        def get_verified_objects() -> str:
-            if review_data["recognized_objects"]:
-                return "  - " + "\n  - ".join(review_data["recognized_objects"])
+        def get_objects_list() -> str:
+            if review_data["unified_objects"]:
+                return "\n- " + "\n- ".join(review_data["unified_objects"])
             else:
-                return "  None"
+                return "\n- (No objects detected)"
 
         context_prompt = f"""
-Please analyze the sequence of images ({len(thumbnails)} total) taken in chronological order from the perspective of the {review_data["camera"].replace("_", " ")} security camera.
+Your task is to analyze the sequence of images ({len(thumbnails)} total) taken in chronological order from the perspective of the {review_data["camera"].replace("_", " ")} security camera.
+
+## Normal Activity Patterns for This Property
 
-**Normal activity patterns for this property:**
 {activity_context_prompt}
 
+## Task Instructions
+
 Your task is to provide a clear, accurate description of the scene that:
 1. States exactly what is happening based on observable actions and movements.
-2. Evaluates whether the observable evidence suggests normal activity for this property or genuine security concerns.
-3. Assigns a potential_threat_level based on the definitions below, applying them consistently.
+2. Evaluates the activity against the Normal and Suspicious Activity Indicators above.
+3. Assigns a potential_threat_level (0, 1, or 2) based on the threat level indicators defined above, applying them consistently.
 
-**IMPORTANT: Start by checking if the activity matches the normal patterns above. If it does, assign Level 0. Only consider higher threat levels if the activity clearly deviates from normal patterns or shows genuine security concerns.**
+**Use the activity patterns above as guidance to calibrate your assessment. Match the activity against both normal and suspicious indicators, then use your judgment based on the complete context.**
+
+## Analysis Guidelines
 
 When forming your description:
-- **CRITICAL: Only describe objects explicitly listed in "Detected objects" below.** Do not infer or mention additional people, vehicles, or objects not present in the detected objects list, even if visual patterns suggest them. If only a car is detected, do not describe a person interacting with it unless "person" is also in the detected objects list.
+- **CRITICAL: Only describe objects explicitly listed in "Objects in Scene" below.** Do not infer or mention additional people, vehicles, or objects not present in this list, even if visual patterns suggest them. If only a car is listed, do not describe a person interacting with it unless "person" is also in the objects list.
 - **Only describe actions actually visible in the frames.** Do not assume or infer actions that you don't observe happening. If someone walks toward furniture but you never see them sit, do not say they sat. Stick to what you can see across the sequence.
 - Describe what you observe: actions, movements, interactions with objects and the environment. Include any observable environmental changes (e.g., lighting changes triggered by activity).
 - Note visible details such as clothing, items being carried or placed, tools or equipment present, and how they interact with the property or objects.
 - Consider the full sequence chronologically: what happens from start to finish, how duration and actions relate to the location and objects involved.
 - **Use the actual timestamp provided in "Activity started at"** below for time of day context—do not infer time from image brightness or darkness. Unusual hours (late night/early morning) should increase suspicion when the observable behavior itself appears questionable. However, recognize that some legitimate activities can occur at any hour.
-- Identify patterns that suggest genuine security concerns: testing doors/windows on vehicles or buildings, accessing unauthorized areas, attempting to conceal actions, extended loitering without apparent purpose, taking items, behavior that clearly doesn't align with the zone context and detected objects.
-- **Weigh all evidence holistically**: Start by checking if the activity matches the normal patterns above. If it does, assign Level 0. Only consider Level 1 if the activity clearly deviates from normal patterns or shows genuine security concerns that warrant attention.
+- **Consider duration as a primary factor**: Apply the duration thresholds defined in the activity patterns above. Brief sequences during normal hours with apparent purpose typically indicate normal activity unless explicit suspicious actions are visible.
+- **Weigh all evidence holistically**: Match the activity against the normal and suspicious patterns defined above, then evaluate based on the complete context (zone, objects, time, actions, duration). Apply the threat level indicators consistently. Use your judgment for edge cases.
+
+## Response Format
 
 Your response MUST be a flat JSON object with:
-- `title` (string): A concise, one-sentence title that captures the main activity. Include any verified recognized objects (from the "Verified recognized objects" list below) and key detected objects. Examples: "Joe walking dog in backyard", "Unknown person testing car doors at night".
+- `title` (string): A concise, direct title that describes the purpose or overall action, not just what you literally see. Use names from "Objects in Scene" based on what you visually observe. If you see both a name and an unidentified object of the same type but visually observe only one person/object, use ONLY the name. Examples: "Joe walking dog", "Person taking out trash", "Joe accessing vehicle", "Joe and person on front porch".
 - `scene` (string): A narrative description of what happens across the sequence from start to finish. **Only describe actions you can actually observe happening in the frames provided.** Do not infer or assume actions that aren't visible (e.g., if you see someone walking but never see them sit, don't say they sat down). Include setting, detected objects, and their observable actions. Avoid speculation or filling in assumed behaviors. Your description should align with and support the threat level you assign.
 - `confidence` (float): 0-1 confidence in your analysis. Higher confidence when objects/actions are clearly visible and context is unambiguous. Lower confidence when the sequence is unclear, objects are partially obscured, or context is ambiguous.
-- `potential_threat_level` (integer): 0, 1, or 2 as defined below. Your threat level must be consistent with your scene description and the guidance above.
+- `potential_threat_level` (integer): 0, 1, or 2 as defined in "Normal Activity Patterns for This Property" above. Your threat level must be consistent with your scene description and the guidance above.
 {get_concern_prompt()}
 
-Threat-level definitions:
-- 0 — **Normal activity (DEFAULT)**: What you observe matches the normal activity patterns above or is consistent with expected activity for this property type. The observable evidence—considering zone context, detected objects, and timing together—supports a benign explanation. **Use this level for routine activities even if minor ambiguous elements exist.**
-- 1 — **Potentially suspicious**: Observable behavior raises genuine security concerns that warrant human review. The evidence doesn't support a routine explanation and clearly deviates from the normal patterns above. Examples: testing doors/windows on vehicles or structures, accessing areas that don't align with the activity, taking items that likely don't belong to them, behavior clearly inconsistent with the zone and context, or activity that lacks any visible legitimate indicators. **Only use this level when the activity clearly doesn't match normal patterns.**
-- 2 — **Immediate threat**: Clear evidence of forced entry, break-in, vandalism, aggression, weapons, theft in progress, or active property damage.
+## Sequence Details
 
-Sequence details:
 - Frame 1 = earliest, Frame {len(thumbnails)} = latest
 - Activity started at {review_data["start"]} and lasted {review_data["duration"]} seconds
-- Detected objects: {", ".join(review_data["objects"])}
-- Verified recognized objects (use these names when describing these objects):
-{get_verified_objects()}
 - Zones involved: {", ".join(z.replace("_", " ").title() for z in review_data["zones"]) or "None"}
 
-**IMPORTANT:**
+## Objects in Scene
+
+Each line represents a detection state, not necessarily unique individuals. Parentheses indicate object type or category, use only the name/label in your response, not the parentheses.
+
+**CRITICAL: When you see both recognized and unrecognized entries of the same type (e.g., "Joe (person)" and "Person"), visually count how many distinct people/objects you actually see based on appearance and clothing. If you observe only ONE person throughout the sequence, use ONLY the recognized name (e.g., "Joe"). The same person may be recognized in some frames but not others. Only describe both if you visually see MULTIPLE distinct people with clearly different appearances.**
+
+**Note: Unidentified objects (without names) are NOT indicators of suspicious activity—they simply mean the system hasn't identified that object.**
+{get_objects_list()}
+
+## Important Notes
 - Values must be plain strings, floats, or integers — no nested objects, no extra commentary.
-- Only describe objects from the "Detected objects" list above. Do not hallucinate additional objects.
+- Only describe objects from the "Objects in Scene" list above. Do not hallucinate additional objects.
+- When describing people or vehicles, use the exact names provided.
 {get_language_prompt()}
 """
         logger.debug(
@@ -149,7 +159,8 @@ Sequence details:
             try:
                 metadata = ReviewMetadata.model_validate_json(clean_json)
 
-                if review_data["recognized_objects"]:
+                # If any verified objects (contain parentheses with name), set to 0
+                if any("(" in obj for obj in review_data["unified_objects"]):
                     metadata.potential_threat_level = 0
 
                 metadata.time = review_data["start"]

frigate/genai/ollama.py

@@ -1,7 +1,7 @@
 """Ollama Provider for Frigate AI."""
 
 import logging
-from typing import Optional
+from typing import Any, Optional
 
 from httpx import TimeoutException
 from ollama import Client as ApiClient
@@ -17,10 +17,24 @@ logger = logging.getLogger(__name__)
 class OllamaClient(GenAIClient):
     """Generative AI client for Frigate using Ollama."""
 
+    LOCAL_OPTIMIZED_OPTIONS = {
+        "options": {
+            "temperature": 0.5,
+            "repeat_penalty": 1.05,
+            "presence_penalty": 0.3,
+        },
+    }
+
     provider: ApiClient
+    provider_options: dict[str, Any]
 
     def _init_provider(self):
         """Initialize the client."""
+        self.provider_options = {
+            **self.LOCAL_OPTIMIZED_OPTIONS,
+            **self.genai_config.provider_options,
+        }
+
         try:
             client = ApiClient(host=self.genai_config.base_url, timeout=self.timeout)
             # ensure the model is available locally
@@ -48,10 +62,13 @@ class OllamaClient(GenAIClient):
                 self.genai_config.model,
                 prompt,
                 images=images if images else None,
-                **self.genai_config.provider_options,
+                **self.provider_options,
+            )
+            logger.debug(
+                f"Ollama tokens used: eval_count={result.get('eval_count')}, prompt_eval_count={result.get('prompt_eval_count')}"
             )
             return result["response"].strip()
-        except (TimeoutException, ResponseError) as e:
+        except (TimeoutException, ResponseError, ConnectionError) as e:
             logger.warning("Ollama returned an error: %s", str(e))
             return None
 

frigate/genai/openai.py

@@ -18,6 +18,7 @@ class OpenAIClient(GenAIClient):
     """Generative AI client for Frigate using OpenAI."""
 
     provider: OpenAI
+    context_size: Optional[int] = None
 
     def _init_provider(self):
         """Initialize the client."""
@@ -69,5 +70,33 @@ class OpenAIClient(GenAIClient):
 
     def get_context_size(self) -> int:
         """Get the context window size for OpenAI."""
-        # OpenAI GPT-4 Vision models have 128K token context window
-        return 128000
+        if self.context_size is not None:
+            return self.context_size
+
+        try:
+            models = self.provider.models.list()
+            for model in models.data:
+                if model.id == self.genai_config.model:
+                    if hasattr(model, "max_model_len") and model.max_model_len:
+                        self.context_size = model.max_model_len
+                        logger.debug(
+                            f"Retrieved context size {self.context_size} for model {self.genai_config.model}"
+                        )
+                        return self.context_size
+
+        except Exception as e:
+            logger.debug(
+                f"Failed to fetch model context size from API: {e}, using default"
+            )
+
+        # Default to 128K for ChatGPT models, 8K for others
+        model_name = self.genai_config.model.lower()
+        if "gpt-4o" in model_name:
+            self.context_size = 128000
+        else:
+            self.context_size = 8192
+
+        logger.debug(
+            f"Using default context size {self.context_size} for model {self.genai_config.model}"
+        )
+        return self.context_size

frigate/output/birdseye.py

@@ -9,6 +9,7 @@ import os
 import queue
 import subprocess as sp
 import threading
+import time
 import traceback
 from typing import Any, Optional
 
@@ -791,6 +792,10 @@ class Birdseye:
         self.frame_manager = SharedMemoryFrameManager()
         self.stop_event = stop_event
         self.requestor = InterProcessRequestor()
+        self.idle_fps: float = self.config.birdseye.idle_heartbeat_fps
+        self._idle_interval: Optional[float] = (
+            (1.0 / self.idle_fps) if self.idle_fps > 0 else None
+        )
 
         if config.birdseye.restream:
             self.birdseye_buffer = self.frame_manager.create(
@@ -848,6 +853,15 @@ class Birdseye:
             if frame_layout_changed:
                 coordinates = self.birdseye_manager.get_camera_coordinates()
                 self.requestor.send_data(UPDATE_BIRDSEYE_LAYOUT, coordinates)
+        if self._idle_interval:
+            now = time.monotonic()
+            is_idle = len(self.birdseye_manager.camera_layout) == 0
+            if (
+                is_idle
+                and (now - self.birdseye_manager.last_output_time)
+                >= self._idle_interval
+            ):
+                self.__send_new_frame()
 
     def stop(self) -> None:
         self.converter.join()

frigate/stats/util.py

@@ -25,6 +25,7 @@ from frigate.util.services import (
     get_intel_gpu_stats,
     get_jetson_stats,
     get_nvidia_gpu_stats,
+    get_openvino_npu_stats,
     get_rockchip_gpu_stats,
     get_rockchip_npu_stats,
     is_vaapi_amd_driver,
@@ -247,6 +248,10 @@ async def set_npu_usages(config: FrigateConfig, all_stats: dict[str, Any]) -> No
             # Rockchip NPU usage
             rk_usage = get_rockchip_npu_stats()
             stats["rockchip"] = rk_usage
+        elif detector.type == "openvino" and detector.device == "NPU":
+            # OpenVINO NPU usage
+            ov_usage = get_openvino_npu_stats()
+            stats["openvino"] = ov_usage
 
     if stats:
         all_stats["npu_usages"] = stats

frigate/timeline.py

@@ -142,6 +142,14 @@ class TimelineProcessor(threading.Thread):
                 timeline_entry[Timeline.data]["attribute"] = list(
                     event_data["attributes"].keys()
                 )[0]
+
+                if len(event_data["current_attributes"]) > 0:
+                    timeline_entry[Timeline.data]["attribute_box"] = to_relative_box(
+                        camera_config.detect.width,
+                        camera_config.detect.height,
+                        event_data["current_attributes"][0]["box"],
+                    )
+
                 save = True
         elif event_type == EventStateEnum.end:
             timeline_entry[Timeline.class_type] = "gone"

frigate/util/classification.py

@@ -2,12 +2,15 @@
 
 import logging
 import os
+import random
+from collections import defaultdict
 
 import cv2
 import numpy as np
 
 from frigate.comms.embeddings_updater import EmbeddingsRequestEnum, EmbeddingsRequestor
 from frigate.comms.inter_process import InterProcessRequestor
+from frigate.config import FfmpegConfig
 from frigate.const import (
     CLIPS_DIR,
     MODEL_CACHE_DIR,
@@ -15,7 +18,10 @@ from frigate.const import (
     UPDATE_MODEL_STATE,
 )
 from frigate.log import redirect_output_to_logger
+from frigate.models import Event, Recordings, ReviewSegment
 from frigate.types import ModelStatusTypesEnum
+from frigate.util.image import get_image_from_recording
+from frigate.util.path import get_event_thumbnail_bytes
 from frigate.util.process import FrigateProcess
 
 BATCH_SIZE = 16
@@ -69,6 +75,7 @@ class ClassificationTrainingProcess(FrigateProcess):
         logger.info(f"Kicking off classification training for {self.model_name}.")
         dataset_dir = os.path.join(CLIPS_DIR, self.model_name, "dataset")
         model_dir = os.path.join(MODEL_CACHE_DIR, self.model_name)
+        os.makedirs(model_dir, exist_ok=True)
         num_classes = len(
             [
                 d
@@ -139,7 +146,6 @@ class ClassificationTrainingProcess(FrigateProcess):
             f.write(tflite_model)
 
 
-@staticmethod
 def kickoff_model_training(
     embeddingRequestor: EmbeddingsRequestor, model_name: str
 ) -> None:
@@ -172,3 +178,520 @@ def kickoff_model_training(
         },
     )
     requestor.stop()
+
+
+@staticmethod
+def collect_state_classification_examples(
+    model_name: str, cameras: dict[str, tuple[float, float, float, float]]
+) -> None:
+    """
+    Collect representative state classification examples from review items.
+
+    This function:
+    1. Queries review items from specified cameras
+    2. Selects 100 balanced timestamps across the data
+    3. Extracts keyframes from recordings (cropped to specified regions)
+    4. Selects 20 most visually distinct images
+    5. Saves them to the dataset directory
+
+    Args:
+        model_name: Name of the classification model
+        cameras: Dict mapping camera names to normalized crop coordinates [x1, y1, x2, y2] (0-1)
+    """
+    dataset_dir = os.path.join(CLIPS_DIR, model_name, "dataset")
+    temp_dir = os.path.join(dataset_dir, "temp")
+    os.makedirs(temp_dir, exist_ok=True)
+
+    # Step 1: Get review items for the cameras
+    camera_names = list(cameras.keys())
+    review_items = list(
+        ReviewSegment.select()
+        .where(ReviewSegment.camera.in_(camera_names))
+        .where(ReviewSegment.end_time.is_null(False))
+        .order_by(ReviewSegment.start_time.asc())
+    )
+
+    if not review_items:
+        logger.warning(f"No review items found for cameras: {camera_names}")
+        return
+
+    # Step 2: Create balanced timestamp selection (100 samples)
+    timestamps = _select_balanced_timestamps(review_items, target_count=100)
+
+    # Step 3: Extract keyframes from recordings with crops applied
+    keyframes = _extract_keyframes(
+        "/usr/lib/ffmpeg/7.0/bin/ffmpeg", timestamps, temp_dir, cameras
+    )
+
+    # Step 4: Select 24 most visually distinct images (they're already cropped)
+    distinct_images = _select_distinct_images(keyframes, target_count=24)
+
+    # Step 5: Save to train directory for later classification
+    train_dir = os.path.join(CLIPS_DIR, model_name, "train")
+    os.makedirs(train_dir, exist_ok=True)
+
+    saved_count = 0
+    for idx, image_path in enumerate(distinct_images):
+        dest_path = os.path.join(train_dir, f"example_{idx:03d}.jpg")
+        try:
+            img = cv2.imread(image_path)
+
+            if img is not None:
+                cv2.imwrite(dest_path, img)
+                saved_count += 1
+        except Exception as e:
+            logger.error(f"Failed to save image {image_path}: {e}")
+
+    import shutil
+
+    try:
+        shutil.rmtree(temp_dir)
+    except Exception as e:
+        logger.warning(f"Failed to clean up temp directory: {e}")
+
+
+def _select_balanced_timestamps(
+    review_items: list[ReviewSegment], target_count: int = 100
+) -> list[dict]:
+    """
+    Select balanced timestamps from review items.
+
+    Strategy:
+    - Group review items by camera and time of day
+    - Sample evenly across groups to ensure diversity
+    - For each selected review item, pick a random timestamp within its duration
+
+    Returns:
+        List of dicts with keys: camera, timestamp, review_item
+    """
+    # Group by camera and hour of day for temporal diversity
+    grouped = defaultdict(list)
+
+    for item in review_items:
+        camera = item.camera
+        # Group by 6-hour blocks for temporal diversity
+        hour_block = int(item.start_time // (6 * 3600))
+        key = f"{camera}_{hour_block}"
+        grouped[key].append(item)
+
+    # Calculate how many samples per group
+    num_groups = len(grouped)
+    if num_groups == 0:
+        return []
+
+    samples_per_group = max(1, target_count // num_groups)
+    timestamps = []
+
+    # Sample from each group
+    for group_items in grouped.values():
+        # Take samples_per_group items from this group
+        sample_size = min(samples_per_group, len(group_items))
+        sampled_items = random.sample(group_items, sample_size)
+
+        for item in sampled_items:
+            # Pick a random timestamp within the review item's duration
+            duration = item.end_time - item.start_time
+            if duration <= 0:
+                continue
+
+            # Sample from middle 80% to avoid edge artifacts
+            offset = random.uniform(duration * 0.1, duration * 0.9)
+            timestamp = item.start_time + offset
+
+            timestamps.append(
+                {
+                    "camera": item.camera,
+                    "timestamp": timestamp,
+                    "review_item": item,
+                }
+            )
+
+    # If we don't have enough, sample more from larger groups
+    while len(timestamps) < target_count and len(timestamps) < len(review_items):
+        for group_items in grouped.values():
+            if len(timestamps) >= target_count:
+                break
+
+            # Pick a random item not already sampled
+            item = random.choice(group_items)
+            duration = item.end_time - item.start_time
+            if duration <= 0:
+                continue
+
+            offset = random.uniform(duration * 0.1, duration * 0.9)
+            timestamp = item.start_time + offset
+
+            # Check if we already have a timestamp near this one
+            if not any(abs(t["timestamp"] - timestamp) < 1.0 for t in timestamps):
+                timestamps.append(
+                    {
+                        "camera": item.camera,
+                        "timestamp": timestamp,
+                        "review_item": item,
+                    }
+                )
+
+    return timestamps[:target_count]
+
+
+def _extract_keyframes(
+    ffmpeg_path: str,
+    timestamps: list[dict],
+    output_dir: str,
+    camera_crops: dict[str, tuple[float, float, float, float]],
+) -> list[str]:
+    """
+    Extract keyframes from recordings at specified timestamps and crop to specified regions.
+
+    Args:
+        ffmpeg_path: Path to ffmpeg binary
+        timestamps: List of timestamp dicts from _select_balanced_timestamps
+        output_dir: Directory to save extracted frames
+        camera_crops: Dict mapping camera names to normalized crop coordinates [x1, y1, x2, y2] (0-1)
+
+    Returns:
+        List of paths to successfully extracted and cropped keyframe images
+    """
+    keyframe_paths = []
+
+    for idx, ts_info in enumerate(timestamps):
+        camera = ts_info["camera"]
+        timestamp = ts_info["timestamp"]
+
+        if camera not in camera_crops:
+            logger.warning(f"No crop coordinates for camera {camera}")
+            continue
+
+        norm_x1, norm_y1, norm_x2, norm_y2 = camera_crops[camera]
+
+        try:
+            recording = (
+                Recordings.select()
+                .where(
+                    (timestamp >= Recordings.start_time)
+                    & (timestamp <= Recordings.end_time)
+                    & (Recordings.camera == camera)
+                )
+                .order_by(Recordings.start_time.desc())
+                .limit(1)
+                .get()
+            )
+        except Exception:
+            continue
+
+        relative_time = timestamp - recording.start_time
+
+        try:
+            config = FfmpegConfig(path="/usr/lib/ffmpeg/7.0")
+            image_data = get_image_from_recording(
+                config,
+                recording.path,
+                relative_time,
+                codec="mjpeg",
+                height=None,
+            )
+
+            if image_data:
+                nparr = np.frombuffer(image_data, np.uint8)
+                img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+
+                if img is not None:
+                    height, width = img.shape[:2]
+
+                    x1 = int(norm_x1 * width)
+                    y1 = int(norm_y1 * height)
+                    x2 = int(norm_x2 * width)
+                    y2 = int(norm_y2 * height)
+
+                    x1_clipped = max(0, min(x1, width))
+                    y1_clipped = max(0, min(y1, height))
+                    x2_clipped = max(0, min(x2, width))
+                    y2_clipped = max(0, min(y2, height))
+
+                    if x2_clipped > x1_clipped and y2_clipped > y1_clipped:
+                        cropped = img[y1_clipped:y2_clipped, x1_clipped:x2_clipped]
+                        resized = cv2.resize(cropped, (224, 224))
+
+                        output_path = os.path.join(output_dir, f"frame_{idx:04d}.jpg")
+                        cv2.imwrite(output_path, resized)
+                        keyframe_paths.append(output_path)
+
+        except Exception as e:
+            logger.debug(
+                f"Failed to extract frame from {recording.path} at {relative_time}s: {e}"
+            )
+            continue
+
+    return keyframe_paths
+
+
+def _select_distinct_images(
+    image_paths: list[str], target_count: int = 20
+) -> list[str]:
+    """
+    Select the most visually distinct images from a set of keyframes.
+
+    Uses a greedy algorithm based on image histograms:
+    1. Start with a random image
+    2. Iteratively add the image that is most different from already selected images
+    3. Difference is measured using histogram comparison
+
+    Args:
+        image_paths: List of paths to candidate images
+        target_count: Number of distinct images to select
+
+    Returns:
+        List of paths to selected images
+    """
+    if len(image_paths) <= target_count:
+        return image_paths
+
+    histograms = {}
+    valid_paths = []
+
+    for path in image_paths:
+        try:
+            img = cv2.imread(path)
+
+            if img is None:
+                continue
+
+            hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+            hist = cv2.calcHist(
+                [hsv], [0, 1, 2], None, [8, 8, 8], [0, 180, 0, 256, 0, 256]
+            )
+            hist = cv2.normalize(hist, hist).flatten()
+            histograms[path] = hist
+            valid_paths.append(path)
+        except Exception as e:
+            logger.debug(f"Failed to process image {path}: {e}")
+            continue
+
+    if len(valid_paths) <= target_count:
+        return valid_paths
+
+    selected = []
+    first_image = random.choice(valid_paths)
+    selected.append(first_image)
+    remaining = [p for p in valid_paths if p != first_image]
+
+    while len(selected) < target_count and remaining:
+        max_min_distance = -1
+        best_candidate = None
+
+        for candidate in remaining:
+            min_distance = float("inf")
+
+            for selected_img in selected:
+                distance = cv2.compareHist(
+                    histograms[candidate],
+                    histograms[selected_img],
+                    cv2.HISTCMP_BHATTACHARYYA,
+                )
+                min_distance = min(min_distance, distance)
+
+            if min_distance > max_min_distance:
+                max_min_distance = min_distance
+                best_candidate = candidate
+
+        if best_candidate:
+            selected.append(best_candidate)
+            remaining.remove(best_candidate)
+        else:
+            break
+
+    return selected
+
+
+@staticmethod
+def collect_object_classification_examples(
+    model_name: str,
+    label: str,
+) -> None:
+    """
+    Collect representative object classification examples from event thumbnails.
+
+    This function:
+    1. Queries events for the specified label
+    2. Selects 100 balanced events across different cameras and times
+    3. Retrieves thumbnails for selected events (with 33% center crop applied)
+    4. Selects 24 most visually distinct thumbnails
+    5. Saves to dataset directory
+
+    Args:
+        model_name: Name of the classification model
+        label: Object label to collect (e.g., "person", "car")
+        cameras: List of camera names to collect examples from
+    """
+    dataset_dir = os.path.join(CLIPS_DIR, model_name, "dataset")
+    temp_dir = os.path.join(dataset_dir, "temp")
+    os.makedirs(temp_dir, exist_ok=True)
+
+    # Step 1: Query events for the specified label and cameras
+    events = list(
+        Event.select().where((Event.label == label)).order_by(Event.start_time.asc())
+    )
+
+    if not events:
+        logger.warning(f"No events found for label '{label}'")
+        return
+
+    logger.debug(f"Found {len(events)} events")
+
+    # Step 2: Select balanced events (100 samples)
+    selected_events = _select_balanced_events(events, target_count=100)
+    logger.debug(f"Selected {len(selected_events)} events")
+
+    # Step 3: Extract thumbnails from events
+    thumbnails = _extract_event_thumbnails(selected_events, temp_dir)
+    logger.debug(f"Successfully extracted {len(thumbnails)} thumbnails")
+
+    # Step 4: Select 24 most visually distinct thumbnails
+    distinct_images = _select_distinct_images(thumbnails, target_count=24)
+    logger.debug(f"Selected {len(distinct_images)} distinct images")
+
+    # Step 5: Save to train directory for later classification
+    train_dir = os.path.join(CLIPS_DIR, model_name, "train")
+    os.makedirs(train_dir, exist_ok=True)
+
+    saved_count = 0
+    for idx, image_path in enumerate(distinct_images):
+        dest_path = os.path.join(train_dir, f"example_{idx:03d}.jpg")
+        try:
+            img = cv2.imread(image_path)
+
+            if img is not None:
+                cv2.imwrite(dest_path, img)
+                saved_count += 1
+        except Exception as e:
+            logger.error(f"Failed to save image {image_path}: {e}")
+
+    import shutil
+
+    try:
+        shutil.rmtree(temp_dir)
+    except Exception as e:
+        logger.warning(f"Failed to clean up temp directory: {e}")
+
+    logger.debug(
+        f"Successfully collected {saved_count} classification examples in {train_dir}"
+    )
+
+
+def _select_balanced_events(
+    events: list[Event], target_count: int = 100
+) -> list[Event]:
+    """
+    Select balanced events from the event list.
+
+    Strategy:
+    - Group events by camera and time of day
+    - Sample evenly across groups to ensure diversity
+    - Prioritize events with higher scores
+
+    Returns:
+        List of selected events
+    """
+    grouped = defaultdict(list)
+
+    for event in events:
+        camera = event.camera
+        hour_block = int(event.start_time // (6 * 3600))
+        key = f"{camera}_{hour_block}"
+        grouped[key].append(event)
+
+    num_groups = len(grouped)
+    if num_groups == 0:
+        return []
+
+    samples_per_group = max(1, target_count // num_groups)
+    selected = []
+
+    for group_events in grouped.values():
+        sorted_events = sorted(
+            group_events,
+            key=lambda e: e.data.get("score", 0) if e.data else 0,
+            reverse=True,
+        )
+
+        sample_size = min(samples_per_group, len(sorted_events))
+        selected.extend(sorted_events[:sample_size])
+
+    if len(selected) < target_count:
+        remaining = [e for e in events if e not in selected]
+        remaining_sorted = sorted(
+            remaining,
+            key=lambda e: e.data.get("score", 0) if e.data else 0,
+            reverse=True,
+        )
+        needed = target_count - len(selected)
+        selected.extend(remaining_sorted[:needed])
+
+    return selected[:target_count]
+
+
+def _extract_event_thumbnails(events: list[Event], output_dir: str) -> list[str]:
+    """
+    Extract thumbnails from events and save to disk.
+
+    Args:
+        events: List of Event objects
+        output_dir: Directory to save thumbnails
+
+    Returns:
+        List of paths to successfully extracted thumbnail images
+    """
+    thumbnail_paths = []
+
+    for idx, event in enumerate(events):
+        try:
+            thumbnail_bytes = get_event_thumbnail_bytes(event)
+
+            if thumbnail_bytes:
+                nparr = np.frombuffer(thumbnail_bytes, np.uint8)
+                img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+
+                if img is not None:
+                    height, width = img.shape[:2]
+
+                    crop_size = 1.0
+                    if event.data and "box" in event.data and "region" in event.data:
+                        box = event.data["box"]
+                        region = event.data["region"]
+
+                        if len(box) == 4 and len(region) == 4:
+                            box_w, box_h = box[2], box[3]
+                            region_w, region_h = region[2], region[3]
+
+                            box_area = (box_w * box_h) / (region_w * region_h)
+
+                            if box_area < 0.05:
+                                crop_size = 0.4
+                            elif box_area < 0.10:
+                                crop_size = 0.5
+                            elif box_area < 0.20:
+                                crop_size = 0.65
+                            elif box_area < 0.35:
+                                crop_size = 0.80
+                            else:
+                                crop_size = 0.95
+
+                    crop_width = int(width * crop_size)
+                    crop_height = int(height * crop_size)
+
+                    x1 = (width - crop_width) // 2
+                    y1 = (height - crop_height) // 2
+                    x2 = x1 + crop_width
+                    y2 = y1 + crop_height
+
+                    cropped = img[y1:y2, x1:x2]
+                    resized = cv2.resize(cropped, (224, 224))
+                    output_path = os.path.join(output_dir, f"thumbnail_{idx:04d}.jpg")
+                    cv2.imwrite(output_path, resized)
+                    thumbnail_paths.append(output_path)
+
+        except Exception as e:
+            logger.debug(f"Failed to extract thumbnail for event {event.id}: {e}")
+            continue
+
+    return thumbnail_paths

frigate/util/config.py

@@ -384,10 +384,10 @@ def migrate_017_0(config: dict[str, dict[str, Any]]) -> dict[str, dict[str, Any]
         new_object_config["genai"] = {}
 
         for key in global_genai.keys():
-            if key not in ["enabled", "model", "provider", "base_url", "api_key"]:
-                new_object_config["genai"][key] = global_genai[key]
-            else:
+            if key in ["model", "provider", "base_url", "api_key"]:
                 new_genai_config[key] = global_genai[key]
+            else:
+                new_object_config["genai"][key] = global_genai[key]
 
         config["genai"] = new_genai_config
 

frigate/util/services.py

@@ -9,6 +9,7 @@ import resource
 import shutil
 import signal
 import subprocess as sp
+import time
 import traceback
 from datetime import datetime
 from typing import Any, List, Optional, Tuple
@@ -388,6 +389,39 @@ def get_intel_gpu_stats(intel_gpu_device: Optional[str]) -> Optional[dict[str, s
         return results
 
 
+def get_openvino_npu_stats() -> Optional[dict[str, str]]:
+    """Get NPU stats using openvino."""
+    NPU_RUNTIME_PATH = "/sys/devices/pci0000:00/0000:00:0b.0/power/runtime_active_time"
+
+    try:
+        with open(NPU_RUNTIME_PATH, "r") as f:
+            initial_runtime = float(f.read().strip())
+
+        initial_time = time.time()
+
+        # Sleep for 1 second to get an accurate reading
+        time.sleep(1.0)
+
+        # Read runtime value again
+        with open(NPU_RUNTIME_PATH, "r") as f:
+            current_runtime = float(f.read().strip())
+
+        current_time = time.time()
+
+        # Calculate usage percentage
+        runtime_diff = current_runtime - initial_runtime
+        time_diff = (current_time - initial_time) * 1000.0  # Convert to milliseconds
+
+        if time_diff > 0:
+            usage = min(100.0, max(0.0, (runtime_diff / time_diff * 100.0)))
+        else:
+            usage = 0.0
+
+        return {"npu": f"{round(usage, 2)}", "mem": "-"}
+    except (FileNotFoundError, PermissionError, ValueError):
+        return None
+
+
 def get_rockchip_gpu_stats() -> Optional[dict[str, str]]:
     """Get GPU stats using rk."""
     try:
@@ -543,7 +577,7 @@ def ffprobe_stream(ffmpeg, path: str, detailed: bool = False) -> sp.CompletedPro
     if detailed and format_entries:
         ffprobe_cmd.extend(["-show_entries", f"format={format_entries}"])
 
-    ffprobe_cmd.extend(["-loglevel", "quiet", clean_path])
+    ffprobe_cmd.extend(["-loglevel", "error", clean_path])
 
     return sp.run(ffprobe_cmd, capture_output=True)
 

web/package-lock.json

@@ -14,6 +14,7 @@
         "@radix-ui/react-alert-dialog": "^1.1.6",
         "@radix-ui/react-aspect-ratio": "^1.1.2",
         "@radix-ui/react-checkbox": "^1.1.4",
+        "@radix-ui/react-collapsible": "^1.1.12",
         "@radix-ui/react-context-menu": "^2.2.6",
         "@radix-ui/react-dialog": "^1.1.15",
         "@radix-ui/react-dropdown-menu": "^2.1.6",
@@ -1380,6 +1381,171 @@
         }
       }
     },
+    "node_modules/@radix-ui/react-collapsible": {
+      "version": "1.1.12",
+      "resolved": "https://registry.npmjs.org/@radix-ui/react-collapsible/-/react-collapsible-1.1.12.tgz",
+      "integrity": "sha512-Uu+mSh4agx2ib1uIGPP4/CKNULyajb3p92LsVXmH2EHVMTfZWpll88XJ0j4W0z3f8NK1eYl1+Mf/szHPmcHzyA==",
+      "license": "MIT",
+      "dependencies": {
+        "@radix-ui/primitive": "1.1.3",
+        "@radix-ui/react-compose-refs": "1.1.2",
+        "@radix-ui/react-context": "1.1.2",
+        "@radix-ui/react-id": "1.1.1",
+        "@radix-ui/react-presence": "1.1.5",
+        "@radix-ui/react-primitive": "2.1.3",
+        "@radix-ui/react-use-controllable-state": "1.2.2",
+        "@radix-ui/react-use-layout-effect": "1.1.1"
+      },
+      "peerDependencies": {
+        "@types/react": "*",
+        "@types/react-dom": "*",
+        "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc",
+        "react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+      },
+      "peerDependenciesMeta": {
+        "@types/react": {
+          "optional": true
+        },
+        "@types/react-dom": {
+          "optional": true
+        }
+      }
+    },
+    "node_modules/@radix-ui/react-collapsible/node_modules/@radix-ui/primitive": {
+      "version": "1.1.3",
+      "resolved": "https://registry.npmjs.org/@radix-ui/primitive/-/primitive-1.1.3.tgz",
+      "integrity": "sha512-JTF99U/6XIjCBo0wqkU5sK10glYe27MRRsfwoiq5zzOEZLHU3A3KCMa5X/azekYRCJ0HlwI0crAXS/5dEHTzDg==",
+      "license": "MIT"
+    },
+    "node_modules/@radix-ui/react-collapsible/node_modules/@radix-ui/react-compose-refs": {
+      "version": "1.1.2",
+      "resolved": "https://registry.npmjs.org/@radix-ui/react-compose-refs/-/react-compose-refs-1.1.2.tgz",
+      "integrity": "sha512-z4eqJvfiNnFMHIIvXP3CY57y2WJs5g2v3X0zm9mEJkrkNv4rDxu+sg9Jh8EkXyeqBkB7SOcboo9dMVqhyrACIg==",
+      "license": "MIT",
+      "peerDependencies": {
+        "@types/react": "*",
+        "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+      },
+      "peerDependenciesMeta": {
+        "@types/react": {
+          "optional": true
+        }
+      }
+    },
+    "node_modules/@radix-ui/react-collapsible/node_modules/@radix-ui/react-context": {
+      "version": "1.1.2",
+      "resolved": "https://registry.npmjs.org/@radix-ui/react-context/-/react-context-1.1.2.tgz",
+      "integrity": "sha512-jCi/QKUM2r1Ju5a3J64TH2A5SpKAgh0LpknyqdQ4m6DCV0xJ2HG1xARRwNGPQfi1SLdLWZ1OJz6F4OMBBNiGJA==",
+      "license": "MIT",
+      "peerDependencies": {
+        "@types/react": "*",
+        "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+      },
+      "peerDependenciesMeta": {
+        "@types/react": {
+          "optional": true
+        }
+      }
+    },
+    "node_modules/@radix-ui/react-collapsible/node_modules/@radix-ui/react-id": {
+      "version": "1.1.1",
+      "resolved": "https://registry.npmjs.org/@radix-ui/react-id/-/react-id-1.1.1.tgz",
+      "integrity": "sha512-kGkGegYIdQsOb4XjsfM97rXsiHaBwco+hFI66oO4s9LU+PLAC5oJ7khdOVFxkhsmlbpUqDAvXw11CluXP+jkHg==",
+      "license": "MIT",
+      "dependencies": {
+        "@radix-ui/react-use-layout-effect": "1.1.1"
+      },
+      "peerDependencies": {
+        "@types/react": "*",
+        "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+      },
+      "peerDependenciesMeta": {
+        "@types/react": {
+          "optional": true
+        }
+      }
+    },
+    "node_modules/@radix-ui/react-collapsible/node_modules/@radix-ui/react-presence": {
+      "version": "1.1.5",
+      "resolved": "https://registry.npmjs.org/@radix-ui/react-presence/-/react-presence-1.1.5.tgz",
+      "integrity": "sha512-/jfEwNDdQVBCNvjkGit4h6pMOzq8bHkopq458dPt2lMjx+eBQUohZNG9A7DtO/O5ukSbxuaNGXMjHicgwy6rQQ==",
+      "license": "MIT",
+      "dependencies": {
+        "@radix-ui/react-compose-refs": "1.1.2",
+        "@radix-ui/react-use-layout-effect": "1.1.1"
+      },
+      "peerDependencies": {
+        "@types/react": "*",
+        "@types/react-dom": "*",
+        "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc",
+        "react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+      },
+      "peerDependenciesMeta": {
+        "@types/react": {
+          "optional": true
+        },
+        "@types/react-dom": {
+          "optional": true
+        }
+      }
+    },
+    "node_modules/@radix-ui/react-collapsible/node_modules/@radix-ui/react-primitive": {
+      "version": "2.1.3",
+      "resolved": "https://registry.npmjs.org/@radix-ui/react-primitive/-/react-primitive-2.1.3.tgz",
+      "integrity": "sha512-m9gTwRkhy2lvCPe6QJp4d3G1TYEUHn/FzJUtq9MjH46an1wJU+GdoGC5VLof8RX8Ft/DlpshApkhswDLZzHIcQ==",
+      "license": "MIT",
+      "dependencies": {
+        "@radix-ui/react-slot": "1.2.3"
+      },
+      "peerDependencies": {
+        "@types/react": "*",
+        "@types/react-dom": "*",
+        "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc",
+        "react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+      },
+      "peerDependenciesMeta": {
+        "@types/react": {
+          "optional": true
+        },
+        "@types/react-dom": {
+          "optional": true
+        }
+      }
+    },
+    "node_modules/@radix-ui/react-collapsible/node_modules/@radix-ui/react-use-controllable-state": {
+      "version": "1.2.2",
+      "resolved": "https://registry.npmjs.org/@radix-ui/react-use-controllable-state/-/react-use-controllable-state-1.2.2.tgz",
+      "integrity": "sha512-BjasUjixPFdS+NKkypcyyN5Pmg83Olst0+c6vGov0diwTEo6mgdqVR6hxcEgFuh4QrAs7Rc+9KuGJ9TVCj0Zzg==",
+      "license": "MIT",
+      "dependencies": {
+        "@radix-ui/react-use-effect-event": "0.0.2",
+        "@radix-ui/react-use-layout-effect": "1.1.1"
+      },
+      "peerDependencies": {
+        "@types/react": "*",
+        "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+      },
+      "peerDependenciesMeta": {
+        "@types/react": {
+          "optional": true
+        }
+      }
+    },
+    "node_modules/@radix-ui/react-collapsible/node_modules/@radix-ui/react-use-layout-effect": {
+      "version": "1.1.1",
+      "resolved": "https://registry.npmjs.org/@radix-ui/react-use-layout-effect/-/react-use-layout-effect-1.1.1.tgz",
+      "integrity": "sha512-RbJRS4UWQFkzHTTwVymMTUv8EqYhOp8dOOviLj2ugtTiXRaRQS7GLGxZTLL1jWhMeoSCf5zmcZkqTl9IiYfXcQ==",
+      "license": "MIT",
+      "peerDependencies": {
+        "@types/react": "*",
+        "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+      },
+      "peerDependenciesMeta": {
+        "@types/react": {
+          "optional": true
+        }
+      }
+    },
     "node_modules/@radix-ui/react-collection": {
       "version": "1.1.2",
       "resolved": "https://registry.npmjs.org/@radix-ui/react-collection/-/react-collection-1.1.2.tgz",

web/package.json

@@ -20,6 +20,7 @@
     "@radix-ui/react-alert-dialog": "^1.1.6",
     "@radix-ui/react-aspect-ratio": "^1.1.2",
     "@radix-ui/react-checkbox": "^1.1.4",
+    "@radix-ui/react-collapsible": "^1.1.12",
     "@radix-ui/react-context-menu": "^2.2.6",
     "@radix-ui/react-dialog": "^1.1.15",
     "@radix-ui/react-dropdown-menu": "^2.1.6",

web/public/locales/ab/views/classificationModel.json

@@ -0,0 +1 @@
+{}

web/public/locales/ar/views/classificationModel.json

@@ -0,0 +1 @@
+{}

web/public/locales/bg/views/classificationModel.json

@@ -0,0 +1 @@
+{}

web/public/locales/ca/audio.json

@@ -425,5 +425,79 @@
   "radio": "Ràdio",
   "pink_noise": "Soroll rosa",
   "power_windows": "Finestres elèctriques",
-  "artillery_fire": "Foc d'artilleria"
+  "artillery_fire": "Foc d'artilleria",
+  "sodeling": "Cant a la tirolesa",
+  "vibration": "Vibració",
+  "throbbing": "Palpitant",
+  "cacophony": "Cacofonia",
+  "sidetone": "To local",
+  "distortion": "Distorsió",
+  "mains_hum": "brunzit",
+  "noise": "Soroll",
+  "echo": "Echo",
+  "reverberation": "Reverberació",
+  "inside": "Interior",
+  "pulse": "Pols",
+  "outside": "Fora",
+  "chirp_tone": "To de grinyol",
+  "harmonic": "Harmònic",
+  "sine_wave": "Ona sinus",
+  "crunch": "Cruixit",
+  "hum": "Taral·lejar",
+  "plop": "Chof",
+  "clickety_clack": "Clic-Clac",
+  "clicking": "Clicant",
+  "clatter": "Soroll",
+  "chird": "Piular",
+  "liquid": "Líquid",
+  "splash": "Xof",
+  "slosh": "Xip-xap",
+  "boing": "Boing",
+  "zing": "Fiu",
+  "rumble": "Bum-bum",
+  "sizzle": "Xiu-xiu",
+  "whir": "Brrrm",
+  "rustle": "Fru-Fru",
+  "creak": "Clic-clac",
+  "clang": "Clang",
+  "squish": "Xaf",
+  "drip": "Plic-plic",
+  "pour": "Glug-glug",
+  "trickle": "Xiulet",
+  "gush": "Xuuuix",
+  "fill": "Glug-glug",
+  "ding": "Ding",
+  "ping": "Ping",
+  "beep": "Bip",
+  "squeal": "Xiscle",
+  "crumpling": "Arrugant-se",
+  "rub": "Fregar",
+  "scrape": "Raspar",
+  "scratch": "Rasca",
+  "whip": "Fuet",
+  "bouncing": "Rebotant",
+  "breaking": "Trencant",
+  "smash": "Aixafar",
+  "whack": "Cop",
+  "slap": "Bufetada",
+  "bang": "Bang",
+  "basketball_bounce": "Rebot de bàsquet",
+  "chorus_effect": "Efecte de cor",
+  "effects_unit": "Unitat d'Efectes",
+  "electronic_tuner": "Afinador electrònic",
+  "thunk": "Bruix",
+  "thump": "Cop fort",
+  "whoosh": "Xiuxiueig",
+  "arrow": "Fletxa",
+  "sonar": "Sonar",
+  "boiling": "Bullint",
+  "stir": "Remenar",
+  "pump": "Bomba",
+  "spray": "Esprai",
+  "shofar": "Xofar",
+  "crushing": "Aixafament",
+  "change_ringing": "Toc de campanes",
+  "flap": "Cop de peu",
+  "roll": "Rodament",
+  "tearing": "Esquinçat"
 }

web/public/locales/ca/common.json

@@ -207,6 +207,14 @@
     "length": {
       "feet": "peus",
       "meters": "metres"
+    },
+    "data": {
+      "kbps": "Kb/s",
+      "mbps": "Mb/s",
+      "gbps": "Gb/s",
+      "kbph": "kB/hora",
+      "mbph": "MB/hora",
+      "gbph": "GB/hora"
     }
   },
   "label": {
@@ -270,5 +278,8 @@
     "desc": "Pàgina no trobada"
   },
   "selectItem": "Selecciona {{item}}",
-  "readTheDocumentation": "Llegir la documentació"
+  "readTheDocumentation": "Llegir la documentació",
+  "information": {
+    "pixels": "{{area}}px"
+  }
 }

web/public/locales/ca/components/auth.json

@@ -10,6 +10,7 @@
       "loginFailed": "Error en l'inici de sessió",
       "unknownError": "Error desconegut. Comproveu els registres.",
       "webUnknownError": "Error desconegut. Comproveu els registres de la consola."
-    }
+    },
+    "firstTimeLogin": "Intentar iniciar sessió per primera vegada? Les credencials s'imprimeixen als registres de Frigate."
   }
 }

web/public/locales/ca/components/dialog.json

@@ -53,7 +53,7 @@
     "export": "Exportar",
     "selectOrExport": "Seleccionar o exportar",
     "toast": {
-      "success": "Exportació inciada amb èxit. Pots veure l'arxiu a la carpeta /exports.",
+      "success": "Exportació inciada amb èxit. Pots veure l'arxiu a la pàgina d'exportacions.",
       "error": {
         "endTimeMustAfterStartTime": "L'hora de finalització ha de ser posterior a l'hora d'inici",
         "noVaildTimeSelected": "No s'ha seleccionat un rang de temps vàlid",
@@ -98,7 +98,8 @@
     "button": {
       "deleteNow": "Suprimir ara",
       "export": "Exportar",
-      "markAsReviewed": "Marcar com a revisat"
+      "markAsReviewed": "Marcar com a revisat",
+      "markAsUnreviewed": "Marcar com no revisat"
     },
     "confirmDelete": {
       "title": "Confirmar la supressió",

web/public/locales/ca/views/classificationModel.json

@@ -0,0 +1,135 @@
+{
+  "documentTitle": "Models de classificació",
+  "button": {
+    "deleteClassificationAttempts": "Suprimeix les imatges de classificació",
+    "renameCategory": "Reanomena la classe",
+    "deleteCategory": "Suprimeix la classe",
+    "deleteImages": "Suprimeix les imatges",
+    "trainModel": "Model de tren"
+  },
+  "toast": {
+    "success": {
+      "deletedCategory": "Classe suprimida",
+      "deletedImage": "Imatges suprimides",
+      "categorizedImage": "Imatge classificada amb èxit",
+      "trainedModel": "Model entrenat amb èxit.",
+      "trainingModel": "S'ha iniciat amb èxit la formació de models."
+    },
+    "error": {
+      "deleteImageFailed": "No s'ha pogut suprimir: {{errorMessage}}",
+      "deleteCategoryFailed": "No s'ha pogut suprimir la classe: {{errorMessage}}",
+      "categorizeFailed": "No s'ha pogut categoritzar la imatge: {{errorMessage}}",
+      "trainingFailed": "No s'ha pogut iniciar l'entrenament del model: {{errorMessage}}"
+    }
+  },
+  "deleteCategory": {
+    "title": "Suprimeix la classe",
+    "desc": "Esteu segur que voleu suprimir la classe {{name}}? Això suprimirà permanentment totes les imatges associades i requerirà tornar a entrenar el model."
+  },
+  "deleteDatasetImages": {
+    "title": "Suprimeix les imatges del conjunt de dades",
+    "desc": "Esteu segur que voleu suprimir {{count}} imatges de {{dataset}}? Aquesta acció no es pot desfer i requerirà tornar a entrenar el model."
+  },
+  "deleteTrainImages": {
+    "title": "Suprimeix les imatges del tren",
+    "desc": "Esteu segur que voleu suprimir {{count}} imatges? Aquesta acció no es pot desfer."
+  },
+  "renameCategory": {
+    "title": "Reanomena la classe",
+    "desc": "Introduïu un nom nou per {{name}}. Se us requerirà que torneu a entrenar el model per al canvi de nom a afectar."
+  },
+  "description": {
+    "invalidName": "Nom no vàlid. Els noms només poden incloure lletres, números, espais, apòstrofs, guions baixos i guions."
+  },
+  "train": {
+    "title": "Classificacions recents",
+    "aria": "Selecciona les classificacions recents"
+  },
+  "categories": "Classes",
+  "createCategory": {
+    "new": "Crea una classe nova"
+  },
+  "categorizeImageAs": "Classifica la imatge com a:",
+  "categorizeImage": "Classifica la imatge",
+  "noModels": {
+    "object": {
+      "title": "No hi ha models de classificació d'objectes",
+      "description": "Crea un model personalitzat per classificar els objectes detectats.",
+      "buttonText": "Crea un model d'objecte"
+    },
+    "state": {
+      "title": "Cap model de classificació d'estat",
+      "description": "Crea un model personalitzat per a monitoritzar i classificar els canvis d'estat en àrees de càmera específiques.",
+      "buttonText": "Crea un model d'estat"
+    }
+  },
+  "wizard": {
+    "title": "Crea una classificació nova",
+    "steps": {
+      "nameAndDefine": "Nom i definició",
+      "stateArea": "Àrea estatal",
+      "chooseExamples": "Trieu exemples"
+    },
+    "step1": {
+      "description": "Els models estatals monitoritzen àrees de càmera fixes per als canvis (p. ex., porta oberta/tancada). Els models d'objectes afegeixen classificacions als objectes detectats (per exemple, animals coneguts, persones de lliurament, etc.).",
+      "name": "Nom",
+      "namePlaceholder": "Introduïu el nom del model...",
+      "type": "Tipus",
+      "typeState": "Estat",
+      "typeObject": "Objecte",
+      "objectLabel": "Etiqueta de l'objecte",
+      "objectLabelPlaceholder": "Selecciona el tipus d'objecte...",
+      "classificationType": "Tipus de classificació",
+      "classificationTypeTip": "Apreneu sobre els tipus de classificació",
+      "classificationTypeDesc": "Les subetiquetes afegeixen text addicional a l'etiqueta de l'objecte (p. ex., 'Person: UPS'). Els atributs són metadades cercables emmagatzemades per separat a les metadades de l'objecte.",
+      "classificationSubLabel": "Subetiqueta",
+      "classificationAttribute": "Atribut",
+      "classes": "Classes",
+      "classesTip": "Aprèn sobre les classes",
+      "classesStateDesc": "Defineix els diferents estats en què pot estar la teva àrea de càmera. Per exemple: \"obert\" i \"tancat\" per a una porta de garatge.",
+      "classesObjectDesc": "Defineix les diferents categories en què classificar els objectes detectats. Per exemple: 'lliuramentpersonpersona', 'resident', 'amenaça' per a la classificació de persones.",
+      "classPlaceholder": "Introduïu el nom de la classe...",
+      "errors": {
+        "nameRequired": "Es requereix el nom del model",
+        "nameLength": "El nom del model ha de tenir 64 caràcters o menys",
+        "nameOnlyNumbers": "El nom del model no pot contenir només números",
+        "classRequired": "Es requereix com a mínim 1 classe",
+        "classesUnique": "Els noms de classe han de ser únics",
+        "stateRequiresTwoClasses": "Els models d'estat requereixen almenys 2 classes",
+        "objectLabelRequired": "Seleccioneu una etiqueta d'objecte",
+        "objectTypeRequired": "Seleccioneu un tipus de classificació"
+      }
+    },
+    "step2": {
+      "description": "Seleccioneu les càmeres i definiu l'àrea a monitoritzar per a cada càmera. El model classificarà l'estat d'aquestes àrees.",
+      "cameras": "Càmeres",
+      "selectCamera": "Selecciona la càmera",
+      "noCameras": "Feu clic a + per a afegir càmeres",
+      "selectCameraPrompt": "Seleccioneu una càmera de la llista per definir la seva àrea de monitoratge"
+    },
+    "step3": {
+      "selectImagesPrompt": "Selecciona totes les imatges amb: {{className}}",
+      "selectImagesDescription": "Feu clic a les imatges per a seleccionar-les. Feu clic a Continua quan hàgiu acabat amb aquesta classe.",
+      "generating": {
+        "title": "S'estan generant imatges de mostra",
+        "description": "Frigate està traient imatges representatives dels vostres enregistraments. Això pot trigar un moment..."
+      },
+      "training": {
+        "title": "Model d'entrenament",
+        "description": "El teu model s'està entrenant en segon pla. Tanqueu aquest diàleg i el vostre model començarà a funcionar tan aviat com s'hagi completat l'entrenament."
+      },
+      "retryGenerate": "Torna a provar la generació",
+      "noImages": "No s'ha generat cap imatge de mostra",
+      "classifying": "Classificació i formació...",
+      "trainingStarted": "L'entrenament s'ha iniciat amb èxit",
+      "errors": {
+        "noCameras": "No s'ha configurat cap càmera",
+        "noObjectLabel": "No s'ha seleccionat cap etiqueta d'objecte",
+        "generateFailed": "No s'han pogut generar exemples: {{error}}",
+        "generationFailed": "Ha fallat la generació. Torneu-ho a provar.",
+        "classifyFailed": "No s'han pogut classificar les imatges: {{error}}"
+      },
+      "generateSuccess": "Imatges de mostra generades amb èxit"
+    }
+  }
+}

web/public/locales/ca/views/events.json

@@ -36,5 +36,17 @@
   "selected_one": "{{count}} seleccionats",
   "selected_other": "{{count}} seleccionats",
   "suspiciousActivity": "Activitat sospitosa",
-  "threateningActivity": "Activitat amenaçadora"
+  "threateningActivity": "Activitat amenaçadora",
+  "detail": {
+    "noDataFound": "No hi ha dades detallades a revisar",
+    "trackedObject_one": "objecte",
+    "aria": "Canvia la vista de detall",
+    "trackedObject_other": "objectes",
+    "noObjectDetailData": "No hi ha dades de detall d'objecte disponibles.",
+    "label": "Detall"
+  },
+  "objectTrack": {
+    "clickToSeek": "Feu clic per cercar aquesta hora",
+    "trackedPoint": "Punt de seguiment"
+  }
 }

web/public/locales/ca/views/explore.json

@@ -206,13 +206,23 @@
     "audioTranscription": {
       "label": "Transcriu",
       "aria": "Demanar una transcripció d'audio"
+    },
+    "showObjectDetails": {
+      "label": "Mostra la ruta de l'objecte"
+    },
+    "hideObjectDetails": {
+      "label": "Amaga la ruta de l'objecte"
+    },
+    "viewTrackingDetails": {
+      "label": "Veure detalls de seguiment",
+      "aria": "Mostra els detalls de seguiment"
     }
   },
   "noTrackedObjects": "No s'han trobat objectes rastrejats",
   "dialog": {
     "confirmDelete": {
       "title": "Confirmar la supressió",
-      "desc": "Eliminant aquest objecte seguit borrarà l'snapshot, qualsevol embedding gravat, i qualsevol objecte associat. Les imatges gravades d'aquest objecte seguit en l'historial <em>NO</em> seràn eliminades.<br /><br />Estas segur que vols continuar?"
+      "desc": "Eliminant aquest objecte seguit borrarà l'snapshot, qualsevol embedding gravat, i qualsevol detall de seguiment. Les imatges gravades d'aquest objecte seguit en l'historial <em>NO</em> seràn eliminades.<br /><br />Estas segur que vols continuar?"
     }
   },
   "fetchingTrackedObjectsFailed": "Error al obtenir objectes rastrejats: {{errorMessage}}",
@@ -224,5 +234,53 @@
   },
   "concerns": {
     "label": "Preocupacions"
+  },
+  "trackingDetails": {
+    "title": "Detalls de seguiment",
+    "noImageFound": "No s'ha trobat cap imatge amb aquesta hora.",
+    "createObjectMask": "Crear màscara d'objecte",
+    "adjustAnnotationSettings": "Ajustar configuració d'anotacions",
+    "scrollViewTips": "Desplaça per veure els moments significants del cicle de vida d'aquest objecte.",
+    "autoTrackingTips": "Limitar les posicións de la caixa serà inacurat per càmeras de seguiment automàtic.",
+    "count": "{{first}} de {{second}}",
+    "trackedPoint": "Punt Seguit",
+    "lifecycleItemDesc": {
+      "visible": "{{label}} detectat",
+      "entered_zone": "{{label}} ha entrat a {{zones}}",
+      "active": "{{label}} ha esdevingut actiu",
+      "stationary": "{{label}} ha esdevingut estacionari",
+      "attribute": {
+        "faceOrLicense_plate": "{{attribute}} detectat per {{label}}",
+        "other": "{{label}} reconegut com a {{attribute}}"
+      },
+      "gone": "{{label}} esquerra",
+      "heard": "{{label}} sentit",
+      "external": "{{label}} detectat",
+      "header": {
+        "zones": "Zones",
+        "ratio": "Ràtio",
+        "area": "Àrea"
+      }
+    },
+    "annotationSettings": {
+      "title": "Configuració d'anotacions",
+      "showAllZones": {
+        "title": "Mostra totes les Zones",
+        "desc": "Mostra sempre les zones amb marcs on els objectes hagin entrat a la zona."
+      },
+      "offset": {
+        "label": "Òfset d'Anotació",
+        "desc": "Aquestes dades provenen del flux de detecció de la càmera, però se superposen a les imatges del flux de gravació. És poc probable que els dos fluxos estiguin perfectament sincronitzats. Com a resultat, el quadre delimitador i el metratge no s'alinearan perfectament. Tanmateix, es pot utilitzar el camp <code>annotation_offset</code> per ajustar-ho.",
+        "millisecondsToOffset": "Millisegons per l'òfset de detecció d'anotacions per. <em>Per defecte: 0</em>",
+        "tips": "CONSELL: Imagineu-vos que hi ha un clip d'esdeveniment amb una persona caminant d'esquerra a dreta. Si el quadre delimitador de la cronologia de l'esdeveniment està constantment a l'esquerra de la persona, aleshores s'hauria de disminuir el valor. De la mateixa manera, si una persona camina d'esquerra a dreta i el quadre delimitador està constantment per davant de la persona, aleshores s'hauria d'augmentar el valor.",
+        "toast": {
+          "success": "L'Òfset d'anotació per a {{camera}} s'ha desat al fitxer de configuració. Reinicieu Frigate per aplicar els canvis."
+        }
+      }
+    },
+    "carousel": {
+      "previous": "Diapositiva anterior",
+      "next": "Dispositiva posterior"
+    }
   }
 }

web/public/locales/ca/views/faceLibrary.json

@@ -12,13 +12,13 @@
   "collections": "Col·leccions",
   "train": {
     "empty": "No hi ha intents recents de reconeixement de rostres",
-    "title": "Entrenar",
-    "aria": "Seleccionar entrenament"
+    "title": "Reconeixements recents",
+    "aria": "Selecciona els reconeixements recents"
   },
   "description": {
-    "addFace": "Guia per a agregar una nova colecció a la biblioteca de rostres.",
+    "addFace": "Afegiu una col·lecció nova a la biblioteca de cares pujant la vostra primera imatge.",
     "placeholder": "Introduïu un nom per a aquesta col·lecció",
-    "invalidName": "Nom no vàlid. Els noms només poden incloure lletres, números, espais, apòstrofs, guions baixos i guionets."
+    "invalidName": "Nom no vàlid. Els noms només poden incloure lletres, números, espais, apòstrofs, guions baixos i guions."
   },
   "documentTitle": "Biblioteca de rostres - Frigate",
   "uploadFaceImage": {
@@ -54,7 +54,7 @@
       "selectImage": "Siusplau, selecciona un fixer d'imatge."
     },
     "maxSize": "Mida màxima: {{size}}MB",
-    "dropInstructions": "Arrastra una imatge aquí, o fes clic per a selccionar-ne una"
+    "dropInstructions": "Arrossegueu i deixeu anar o enganxeu una imatge aquí, o feu clic per seleccionar"
   },
   "button": {
     "uploadImage": "Pujar imatge",

web/public/locales/ca/views/live.json

@@ -86,8 +86,8 @@
     "disable": "Amaga estadístiques de la transmissió"
   },
   "manualRecording": {
-    "title": "Gravació sota demanda",
-    "tips": "Iniciar un event manual basat en els paràmetres de retenció de gravació per aquesta càmera.",
+    "title": "Sota demanda",
+    "tips": "Baixeu una instantània o inicieu un esdeveniment manual basat en la configuració de retenció d'enregistrament d'aquesta càmera.",
     "playInBackground": {
       "label": "Reproduir en segon pla",
       "desc": "Habilita aquesta opció per a continuar la transmissió quan el reproductor està amagat."
@@ -130,6 +130,9 @@
     "playInBackground": {
       "label": "Reproduir en segon pla",
       "tips": "Habilita aquesta opció per a contiuar la transmissió tot i que el reproductor estigui ocult."
+    },
+    "debug": {
+      "picker": "Selecció de stream no disponible en mode debug. La vista debug sempre fa servir el stream assignat pel rol de detecció."
     }
   },
   "streamingSettings": "Paràmetres de transmissió",
@@ -167,5 +170,16 @@
   "transcription": {
     "enable": "Habilita la transcripció d'àudio en temps real",
     "disable": "Deshabilita la transcripció d'àudio en temps real"
+  },
+  "snapshot": {
+    "takeSnapshot": "Descarregar una instantània",
+    "noVideoSource": "No hi ha cap font de video per fer una instantània.",
+    "captureFailed": "Error capturant una instantània.",
+    "downloadStarted": "Inici de baixada d'instantània."
+  },
+  "noCameras": {
+    "title": "No s'ha configurat cap càmera",
+    "description": "Comenceu connectant una càmera a Frigate.",
+    "buttonText": "Afegeix una càmera"
   }
 }

web/public/locales/ca/views/settings.json

@@ -9,7 +9,9 @@
     "masksAndZones": "Editor de màscares i zones - Frigate",
     "general": "Paràmetres Generals - Frigate",
     "frigatePlus": "Paràmetres de Frigate+ - Frigate",
-    "notifications": "Paràmetres de notificació - Frigate"
+    "notifications": "Paràmetres de notificació - Frigate",
+    "cameraManagement": "Gestionar càmeres - Frigate",
+    "cameraReview": "Configuració Revisió de Càmeres - Frigate"
   },
   "menu": {
     "ui": "Interfície d'usuari",
@@ -21,7 +23,10 @@
     "debug": "Depuració",
     "frigateplus": "Frigate+",
     "enrichments": "Enriquiments",
-    "triggers": "Disparadors"
+    "triggers": "Disparadors",
+    "cameraManagement": "Gestió",
+    "cameraReview": "Revisió",
+    "roles": "Rols"
   },
   "dialog": {
     "unsavedChanges": {
@@ -109,7 +114,8 @@
           "mustBeAtLeastTwoCharacters": "El nom de la zona ha de contenir com a mínim 2 caràcters.",
           "mustNotContainPeriod": "El nom de la zona no pot contenir punts.",
           "alreadyExists": "Ja existeix una zona amb aquest nom per a aquesta càmera.",
-          "mustNotBeSameWithCamera": "El nom de la zona no pot ser el mateix que el nom de la càmera."
+          "mustNotBeSameWithCamera": "El nom de la zona no pot ser el mateix que el nom de la càmera.",
+          "mustHaveAtLeastOneLetter": "El nom de la zona ha de tenir almenys una lletra."
         }
       },
       "inertia": {
@@ -158,7 +164,7 @@
       "name": {
         "inputPlaceHolder": "Introduïu un nom…",
         "title": "Nom",
-        "tips": "El nom ha de tenir almenys 2 caràcters i no pot coincidir amb el nom d'una càmera ni amb el d'una altra zona."
+        "tips": "El nom ha de tenir almenys 2 caràcters, ha de tenir almenys una lletra, i no ha de ser el nom d'una càmera o una altra zona."
       },
       "label": "Zones",
       "desc": {
@@ -744,6 +750,11 @@
           "error": {
             "min": "S'ha de seleccionar una acció com a mínim."
           }
+        },
+        "friendly_name": {
+          "title": "Nom amistós",
+          "placeholder": "Nom o descripció d'aquest disparador",
+          "description": "Un nom opcional amistós o text descriptiu per a aquest activador."
         }
       }
     },
@@ -764,7 +775,11 @@
       "title": "Gestió de disparadors",
       "desc": "Gestionar els disparadors de {{camera}}. Usa les tipus de miniatures per disparar miniatures similars a l'objecte a seguir seleccionat, i el tipus de descripció per disparar en cas de descripcions similars a l'especificada."
     },
-    "addTrigger": "Afegir disaprador"
+    "addTrigger": "Afegir disaprador",
+    "semanticSearch": {
+      "desc": "La cerca semàntica ha d'estar activada per a utilitzar els activadors.",
+      "title": "La cerca semàntica està desactivada"
+    }
   },
   "roles": {
     "dialog": {
@@ -825,5 +840,229 @@
         "userUpdateFailed": "Error a l'actualitzar els ros d'usuari: {{errorMessage}}"
       }
     }
+  },
+  "cameraWizard": {
+    "title": "Afegir C àmera",
+    "description": "Seguiu els passos de sota per afegir una nova càmera a la instal·lació.",
+    "steps": {
+      "nameAndConnection": "Nom i connexió",
+      "streamConfiguration": "Configuració de stream",
+      "validationAndTesting": "Validació i proves"
+    },
+    "step1": {
+      "cameraBrand": "Marca de la càmera",
+      "description": "Introduïu els detalls de la càmera i proveu la connexió.",
+      "cameraName": "Nom de la càmera",
+      "cameraNamePlaceholder": "p. ex., vista general de la porta davantera o de la barra posterior",
+      "host": "Adreça de l'amfitrió/IP",
+      "port": "Port",
+      "username": "Nom d'usuari",
+      "usernamePlaceholder": "Opcional",
+      "password": "Contrasenya",
+      "passwordPlaceholder": "Opcional",
+      "selectTransport": "Selecciona el protocol de transport",
+      "brandInformation": "Informació de marca",
+      "brandUrlFormat": "Per a càmeres amb el format d'URL RTSP com: {{exampleUrl}}",
+      "customUrlPlaceholder": "rtsp://usuari:contrasenya@host:port/ruta",
+      "testConnection": "Prova la connexió",
+      "testSuccess": "Prova de connexió correcta!",
+      "testFailed": "Ha fallat la prova de connexió. Si us plau, comproveu la vostra entrada i torneu-ho a provar.",
+      "streamDetails": "Detalls del flux",
+      "warnings": {
+        "noSnapshot": "No s'ha pogut obtenir una instantània del flux configurat."
+      },
+      "errors": {
+        "brandOrCustomUrlRequired": "Seleccioneu una marca de càmera amb host/IP o trieu 'Altres' amb un URL personalitzat",
+        "nameRequired": "Es requereix el nom de la càmera",
+        "nameLength": "El nom de la càmera ha de tenir 64 caràcters o menys",
+        "invalidCharacters": "El nom de la càmera conté caràcters no vàlids",
+        "nameExists": "El nom de la càmera ja existeix",
+        "brands": {
+          "reolink-rtsp": "No es recomana Reolink RST. Es recomana habilitar HTTP a la configuració de la càmera i reiniciar l'assistent de la càmera."
+        },
+        "customUrlRtspRequired": "Els URL personalitzats han de començar amb \"rtsp://\". Es requereix configuració manual per a fluxos de càmera no RTSP."
+      },
+      "selectBrand": "Seleccioneu la marca de la càmera per a la plantilla d'URL",
+      "customUrl": "URL de flux personalitzat",
+      "docs": {
+        "reolink": "https://docs.frigate.video/configuration/camera_specific.html#reolink-cameras"
+      },
+      "testing": {
+        "probingMetadata": "S'estan provant les metadades de la càmera...",
+        "fetchingSnapshot": "S'està recuperant la instantània de la càmera..."
+      }
+    },
+    "save": {
+      "failure": "SS'ha produït un error en desar {{cameraName}}.",
+      "success": "S'ha desat correctament la càmera nova {{cameraName}}."
+    },
+    "testResultLabels": {
+      "resolution": "Resolució",
+      "video": "Vídeo",
+      "audio": "Àudio",
+      "fps": "FPS"
+    },
+    "commonErrors": {
+      "noUrl": "Proporcioneu un URL de flux vàlid",
+      "testFailed": "Ha fallat la prova de flux: {{error}}"
+    },
+    "step2": {
+      "description": "Configura els rols de flux i afegeix fluxos addicionals per a la càmera.",
+      "streamsTitle": "Fluxos de la càmera",
+      "addStream": "Afegeix un flux",
+      "addAnotherStream": "Afegeix un altre flux",
+      "streamTitle": "Flux {{number}}",
+      "streamUrl": "URL del flux",
+      "url": "URL",
+      "resolution": "Resolució",
+      "selectResolution": "Selecciona la resolució",
+      "quality": "Qualitat",
+      "selectQuality": "Selecciona la qualitat",
+      "roleLabels": {
+        "detect": "Detecció d'objectes",
+        "record": "Enregistrament",
+        "audio": "Àudio"
+      },
+      "testStream": "Prova la connexió",
+      "testSuccess": "Prova de flux amb èxit!",
+      "testFailed": "Ha fallat la prova del flux",
+      "testFailedTitle": "Ha fallat la prova",
+      "connected": "Connectat",
+      "notConnected": "No connectat",
+      "featuresTitle": "Característiques",
+      "go2rtc": "Redueix les connexions a la càmera",
+      "detectRoleWarning": "Almenys un flux ha de tenir el rol de \"detecte\" per continuar.",
+      "rolesPopover": {
+        "title": "Rols de flux",
+        "detect": "Canal principal per a la detecció d'objectes.",
+        "record": "Desa els segments del canal de vídeo basats en la configuració.",
+        "audio": "Canal per a la detecció basada en àudio."
+      },
+      "featuresPopover": {
+        "title": "Característiques del flux",
+        "description": "Utilitzeu el restreaming go2rtc per reduir les connexions a la càmera."
+      },
+      "roles": "Rols",
+      "streamUrlPlaceholder": "rtsp://usuari:contrasenya@host:port/ruta"
+    },
+    "step3": {
+      "none": "Cap",
+      "error": "Error",
+      "saveAndApply": "Desa una càmera nova",
+      "saveError": "Configuració no vàlida. Si us plau, comproveu la configuració.",
+      "issues": {
+        "title": "Validació del flux",
+        "videoCodecGood": "El còdec de vídeo és {{codec}}.",
+        "audioCodecGood": "El còdec d'àudio és {{codec}}.",
+        "noAudioWarning": "No s'ha detectat cap àudio per a aquest flux, els enregistraments no tindran àudio.",
+        "audioCodecRecordError": "El còdec d'àudio AAC és necessari per a suportar l'àudio en els enregistraments.",
+        "audioCodecRequired": "Es requereix un flux d'àudio per admetre la detecció d'àudio.",
+        "restreamingWarning": "Reduir les connexions a la càmera per al flux de registre pot augmentar lleugerament l'ús de la CPU.",
+        "dahua": {
+          "substreamWarning": "El substream 1 està bloquejat a una resolució baixa. Moltes càmeres Dahua / Amcrest / EmpireTech suporten subfluxos addicionals que han d'estar habilitats a la configuració de la càmera. Es recomana comprovar i utilitzar aquests corrents si estan disponibles."
+        },
+        "hikvision": {
+          "substreamWarning": "El substream 1 està bloquejat a una resolució baixa. Moltes càmeres Hikvision suporten subfluxos addicionals que han d'estar habilitats a la configuració de la càmera. Es recomana comprovar i utilitzar aquests corrents si estan disponibles."
+        },
+        "resolutionHigh": "Una resolució de {{resolution}} pot causar un ús més gran dels recursos.",
+        "resolutionLow": "Una resolució de {{resolution}} pot ser massa baixa per a la detecció fiable d'objectes petits."
+      },
+      "description": "Validació i anàlisi final abans de desar la nova càmera. Connecta cada flux abans de desar-lo.",
+      "validationTitle": "Validació del flux",
+      "connectAllStreams": "Connecta tots els fluxos",
+      "reconnectionSuccess": "S'ha reconnectat correctament.",
+      "reconnectionPartial": "Alguns fluxos no s'han pogut tornar a connectar.",
+      "streamUnavailable": "La vista prèvia del flux no està disponible",
+      "reload": "Torna a carregar",
+      "connecting": "Connectant...",
+      "streamTitle": "Flux {{number}}",
+      "valid": "Vàlid",
+      "failed": "Ha fallat",
+      "notTested": "No provat",
+      "connectStream": "Connecta",
+      "connectingStream": "Connectant",
+      "disconnectStream": "Desconnecta",
+      "estimatedBandwidth": "Amplada de banda estimad",
+      "roles": "Rols",
+      "streamValidated": "El flux {{number}} s'ha validat correctament",
+      "streamValidationFailed": "Ha fallat la validació del flux {{number}}"
+    }
+  },
+  "cameraManagement": {
+    "title": "Gestiona les càmeres",
+    "addCamera": "Afegeix una càmera nova",
+    "editCamera": "Edita la càmera:",
+    "selectCamera": "Selecciona una càmera",
+    "backToSettings": "Torna a la configuració de la càmera",
+    "streams": {
+      "title": "Habilita / Inhabilita les càmeres",
+      "desc": "Inhabilita temporalment una càmera fins que es reiniciï la fragata. La inhabilitació d'una càmera atura completament el processament de Frigate dels fluxos d'aquesta càmera. La detecció, l'enregistrament i la depuració no estaran disponibles.<br /> <em>Nota: això no desactiva les retransmissions de go2rtc.</em>"
+    },
+    "cameraConfig": {
+      "add": "Afegeix una càmera",
+      "edit": "Edita la càmera",
+      "description": "Configura la configuració de la càmera, incloses les entrades i els rols de flux.",
+      "name": "Nom de la càmera",
+      "nameRequired": "Es requereix el nom de la càmera",
+      "nameLength": "El nom de la càmera ha de tenir menys de 64 caràcters.",
+      "namePlaceholder": "p. ex., vista general de la porta davantera o de la barra posterior",
+      "enabled": "Habilitat",
+      "ffmpeg": {
+        "inputs": "Fluxos d'entrada",
+        "path": "Camí del flux",
+        "pathRequired": "Es requereix un camí de flux",
+        "pathPlaceholder": "rtsp://...",
+        "rolesRequired": "Es requereix almenys un rol",
+        "rolesUnique": "Cada rol (àudio, detecta, registra) només es pot assignar a un flux",
+        "addInput": "Afegeix un flux d'entrada",
+        "removeInput": "Elimina el flux d'entrada",
+        "inputsRequired": "Es requereix com a mínim un flux d'entrada",
+        "roles": "Rols"
+      },
+      "go2rtcStreams": "go2rtc Fluxos",
+      "streamUrls": "URL de flux",
+      "addUrl": "Afegeix un URL",
+      "addGo2rtcStream": "Afegeix go2rtc flux",
+      "toast": {
+        "success": "La càmera {{cameraName}} s'ha desat correctament"
+      }
+    }
+  },
+  "cameraReview": {
+    "object_descriptions": {
+      "title": "Descripcions d'objectes generadors d'IA",
+      "desc": "Activa/desactiva temporalment les descripcions d'objectes generatius d'IA per a aquesta càmera. Quan està desactivat, les descripcions generades per IA no se sol·licitaran per als objectes rastrejats en aquesta càmera."
+    },
+    "review_descriptions": {
+      "title": "Descripcions de la IA generativa",
+      "desc": "Activa/desactiva temporalment les descripcions de revisió de la IA generativa per a aquesta càmera. Quan està desactivat, les descripcions generades per IA no se sol·licitaran per als elements de revisió d'aquesta càmera."
+    },
+    "review": {
+      "title": "Revisió",
+      "desc": "Activa/desactiva temporalment les alertes i deteccions d'aquesta càmera fins que es reiniciï Frigate. Si està desactivat, no es generaran nous elements de revisió. ",
+      "alerts": "Alertes. ",
+      "detections": "Deteccions. "
+    },
+    "reviewClassification": {
+      "title": "Revisió de la classificació",
+      "desc": "Frigate categoritza els articles de revisió com Alertes i Deteccions. Per defecte, tots els objectes <em>persona</em> i <em>cotxe</em> es consideren Alertes. Podeu refinar la categorització dels elements de revisió configurant-los les zones requerides.",
+      "noDefinedZones": "No hi ha zones definides per a aquesta càmera.",
+      "selectAlertsZones": "Selecciona zones per a les alertes",
+      "selectDetectionsZones": "Selecció de zones per a les deteccions",
+      "limitDetections": "Limita les deteccions a zones específiques",
+      "toast": {
+        "success": "S'ha desat la configuració de la classificació de la revisió. Reinicia la fragata per aplicar canvis."
+      },
+      "unsavedChanges": "Paràmetres de classificació de revisions sense desar per {{camera}}",
+      "objectAlertsTips": "Totes els objectes {{alertsLabels}} de {{cameraName}} es mostraran com avisos.",
+      "zoneObjectAlertsTips": "Tots els objectes{{alertsLabels}} detectats en {{zone}} de {{cameraName}} es mostraran com a avisos.",
+      "objectDetectionsTips": "Tots els objectes {{detectionsLabels}} que no estiguin categoritzats de {{cameraName}} es mostraran com a Deteccions independentment de la zona on es trobin.",
+      "zoneObjectDetectionsTips": {
+        "text": "Tots els objectes {{detectionsLabels}} no categoritzats a {{zone}} de {{cameraName}} es mostraran com a Deteccions.",
+        "notSelectDetections": "Tots els objectes {{detectionsLabels}} detectats a {{zone}} de{{cameraName}} no categoritzats com a alertes es mostraran com a Deteccions independentment de la zona on es trobin.",
+        "regardlessOfZoneObjectDetectionsTips": "Tots els objectes {{detectionsLabels}} que no estiguin categoritzats de {{cameraName}} es mostraran com a Deteccions independentment de la zona on es trobin."
+      }
+    },
+    "title": "Paràmetres de Revisió de la Càmera"
   }
 }

web/public/locales/cs/views/classificationModel.json

@@ -0,0 +1 @@
+{}

web/public/locales/cs/views/settings.json

@@ -10,7 +10,8 @@
     "object": "Ladění - Frigate",
     "general": "Obecné nastavení - Frigate",
     "frigatePlus": "Frigate+ nastavení - Frigate",
-    "enrichments": "Nastavení obohacení - Frigate"
+    "enrichments": "Nastavení obohacení - Frigate",
+    "cameraManagement": "Správa kamer - Frigate"
   },
   "frigatePlus": {
     "toast": {

web/public/locales/da/views/classificationModel.json

@@ -0,0 +1 @@
+{}

web/public/locales/de/common.json

@@ -232,6 +232,14 @@
     "length": {
       "feet": "Fuß",
       "meters": "Meter"
+    },
+    "data": {
+      "kbps": "kB/s",
+      "mbps": "MB/s",
+      "gbps": "GB/s",
+      "kbph": "kB/Stunde",
+      "mbph": "MB/Stunde",
+      "gbph": "GB/Stunde"
     }
   },
   "toast": {
@@ -273,5 +281,8 @@
     "desc": "Du hast keine Berechtigung diese Seite anzuzeigen.",
     "documentTitle": "Zugang verweigert - Frigate",
     "title": "Zugang verweigert"
+  },
+  "information": {
+    "pixels": "{{area}}px"
   }
 }

web/public/locales/de/components/dialog.json

@@ -117,7 +117,8 @@
     "button": {
       "export": "Exportieren",
       "markAsReviewed": "Als geprüft markieren",
-      "deleteNow": "Jetzt löschen"
+      "deleteNow": "Jetzt löschen",
+      "markAsUnreviewed": "Als ungeprüft markieren"
     }
   },
   "imagePicker": {

web/public/locales/de/objects.json

@@ -27,7 +27,7 @@
   "donut": "Donut",
   "cake": "Kuchen",
   "chair": "Stuhl",
-  "couch": "Couch",
+  "couch": "Sofa",
   "bed": "Bett",
   "dining_table": "Esstisch",
   "toilet": "Toilette",

web/public/locales/de/views/classificationModel.json

@@ -0,0 +1 @@
+{}

web/public/locales/de/views/faceLibrary.json

@@ -29,7 +29,7 @@
   "selectFace": "Wähle Gesicht",
   "imageEntry": {
     "dropActive": "Ziehe das Bild hierher…",
-    "dropInstructions": "Ziehe ein Bild hier her oder klicke um eines auszuwählen",
+    "dropInstructions": "Ziehe ein Bild hier her, füge es ein oder klicke um eines auszuwählen",
     "maxSize": "Maximale Größe: {{size}} MB",
     "validation": {
       "selectImage": "Bitte wähle ein Bild aus."

web/public/locales/de/views/live.json

@@ -30,16 +30,16 @@
     },
     "zoom": {
       "in": {
-        "label": "PTZ-Kamera vergrößern"
+        "label": "PTZ-Kamera rein zoomen"
       },
       "out": {
-        "label": "PTZ-Kamera herauszoomen"
+        "label": "PTZ-Kamera heraus zoomen"
       }
     },
-    "presets": "PTZ-Kameravoreinstellungen",
+    "presets": "PTZ-Kamera Voreinstellungen",
     "frame": {
       "center": {
-        "label": "Klicken Sie in den Rahmen, um die PTZ-Kamera zu zentrieren"
+        "label": "Klicke in den Rahmen, um die PTZ-Kamera zu zentrieren"
       }
     },
     "focus": {
@@ -62,8 +62,8 @@
     "enable": "Aufzeichnung aktivieren"
   },
   "snapshots": {
-    "enable": "Snapshots aktivieren",
-    "disable": "Snapshots deaktivieren"
+    "enable": "Schnappschüsse aktivieren",
+    "disable": "Schnappschüsse deaktivieren"
   },
   "autotracking": {
     "disable": "Autotracking deaktivieren",
@@ -74,7 +74,7 @@
     "disable": "Stream-Statistiken ausblenden"
   },
   "manualRecording": {
-    "title": "On-Demand Aufzeichnung",
+    "title": "On-Demand",
     "showStats": {
       "label": "Statistiken anzeigen",
       "desc": "Aktivieren Sie diese Option, um Stream-Statistiken als Overlay über dem Kamera-Feed anzuzeigen."
@@ -88,7 +88,7 @@
       "desc": "Aktivieren Sie diese Option, um das Streaming fortzusetzen, wenn der Player ausgeblendet ist.",
       "label": "Im Hintergrund abspielen"
     },
-    "tips": "Starten Sie ein manuelles Ereignis basierend auf den Aufzeichnung Aufbewahrungseinstellungen dieser Kamera.",
+    "tips": "Lade einen Sofort-Schnappschuss herunter oder starte ein manuelles Ereignis basierend auf den Aufbewahrungseinstellungen für Aufzeichnungen dieser Kamera.",
     "debugView": "Debug-Ansicht",
     "start": "On-Demand Aufzeichnung starten",
     "failedToEnd": "Die manuelle On-Demand Aufzeichnung konnte nicht beendet werden."
@@ -118,6 +118,9 @@
     "playInBackground": {
       "tips": "Aktivieren Sie diese Option, um das Streaming fortzusetzen, wenn der Player ausgeblendet ist.",
       "label": "Im Hintergrund abspielen"
+    },
+    "debug": {
+      "picker": "Stream Auswahl nicht verfügbar im Debug Modus. Die Debug Ansicht nutzt immer den Stream, welcher der Rolle zugewiesen ist."
     }
   },
   "effectiveRetainMode": {
@@ -167,5 +170,16 @@
   "transcription": {
     "enable": "Live Audio Transkription einschalten",
     "disable": "Live Audio Transkription ausschalten"
+  },
+  "noCameras": {
+    "title": "Keine Kameras eingerichtet",
+    "description": "Beginne indem du eine Kamera anschließt.",
+    "buttonText": "Kamera hinzufügen"
+  },
+  "snapshot": {
+    "takeSnapshot": "Sofort-Schnappschuss herunterladen",
+    "noVideoSource": "Keine Video-Quelle für Schnappschuss verfügbar.",
+    "captureFailed": "Die Aufnahme des Schnappschusses ist fehlgeschlagen.",
+    "downloadStarted": "Schnappschuss Download gestartet."
   }
 }

web/public/locales/de/views/settings.json

@@ -10,7 +10,9 @@
     "classification": "Klassifizierungseinstellungen – Frigate",
     "motionTuner": "Bewegungserkennungs-Optimierer – Frigate",
     "notifications": "Benachrichtigungs-Einstellungen",
-    "enrichments": "Erweiterte Statistiken - Frigate"
+    "enrichments": "Erweiterte Statistiken - Frigate",
+    "cameraManagement": "Kameras verwalten - Frigate",
+    "cameraReview": "Kamera Einstellungen prüfen - Frigate"
   },
   "menu": {
     "ui": "Benutzeroberfläche",
@@ -23,7 +25,10 @@
     "users": "Benutzer",
     "notifications": "Benachrichtigungen",
     "enrichments": "Erkennungsfunktionen",
-    "triggers": "Auslöser"
+    "triggers": "Auslöser",
+    "roles": "Rollen",
+    "cameraManagement": "Verwaltung",
+    "cameraReview": "Überprüfung"
   },
   "dialog": {
     "unsavedChanges": {
@@ -69,7 +74,7 @@
       "title": "Kalender",
       "firstWeekday": {
         "label": "Erster Wochentag",
-        "desc": "Der Tag, an dem die Wochen des Review Kalenders beginnen.",
+        "desc": "Der Tag, an dem die Wochen des Überprüfungs-Kalenders beginnen.",
         "sunday": "Sonntag",
         "monday": "Montag"
       }
@@ -812,6 +817,11 @@
           "error": {
             "min": "Mindesten eine Aktion muss ausgewählt sein."
           }
+        },
+        "friendly_name": {
+          "title": "Nutzerfreundlicher Name",
+          "placeholder": "Benenne oder beschreibe diesen Auslöser",
+          "description": "Ein optionaler nutzerfreundlicher Name oder eine Beschreibung für diesen Auslöser."
         }
       }
     },
@@ -826,6 +836,10 @@
         "updateTriggerFailed": "Auslöser könnte nicht aktualisiert werden: {{errorMessage}}",
         "deleteTriggerFailed": "Auslöser konnte nicht gelöscht werden: {{errorMessage}}"
       }
+    },
+    "semanticSearch": {
+      "title": "Semantische Suche ist deaktiviert",
+      "desc": "Semantische Suche muss aktiviert sein um Auslöser nutzen zu können."
     }
   },
   "roles": {
@@ -887,5 +901,222 @@
         "userUpdateFailed": "Aktualisierung der Benutzerrollen fehlgeschlagen: {{errorMessage}}"
       }
     }
+  },
+  "cameraWizard": {
+    "title": "Kamera hinzufügen",
+    "description": "Folge den Anweisungen unten, um eine neue Kamera zu deiner Frigate-Installation hinzuzufügen.",
+    "steps": {
+      "nameAndConnection": "Name & Verbindung",
+      "streamConfiguration": "Stream Konfiguration",
+      "validationAndTesting": "Überprüfung & Testen"
+    },
+    "save": {
+      "success": "Neue Kamera {{cameraName}} erfolgreich hinzugefügt.",
+      "failure": "Fehler beim Speichern von {{cameraName}}."
+    },
+    "testResultLabels": {
+      "resolution": "Auflösung",
+      "video": "Video",
+      "audio": "Audio",
+      "fps": "FPS"
+    },
+    "commonErrors": {
+      "noUrl": "Bitte korrekte Stream-URL eingeben",
+      "testFailed": "Stream Test fehlgeschlagen: {{error}}"
+    },
+    "step1": {
+      "description": "Gib deine Kameradaten ein und teste die Verbindung.",
+      "cameraName": "Kamera-Name",
+      "cameraNamePlaceholder": "z.B. vordere_tür oder Hof Übersicht",
+      "host": "Host/IP Adresse",
+      "port": "Port",
+      "username": "Nutzername",
+      "usernamePlaceholder": "Optional",
+      "password": "Passwort",
+      "passwordPlaceholder": "Optional",
+      "selectTransport": "Transport-Protokoll auswählen",
+      "cameraBrand": "Kamera-Hersteller",
+      "selectBrand": "Wähle die Kamera-Hersteller für die URL-Vorlage aus",
+      "customUrl": "Benutzerdefinierte Stream-URL",
+      "brandInformation": "Hersteller Information",
+      "brandUrlFormat": "Für Kameras mit RTSP URL nutze folgendes Format: {{exampleUrl}}",
+      "customUrlPlaceholder": "rtsp://nutzername:passwort@host:port/pfad",
+      "testConnection": "Teste Verbindung",
+      "testSuccess": "Verbindungstest erfolgreich!",
+      "testFailed": "Verbindungstest fehlgeschlagen. Bitte prüfe deine Eingaben und versuche es erneut.",
+      "streamDetails": "Stream Details",
+      "warnings": {
+        "noSnapshot": "Es kann kein Snapshot aus dem konfigurierten Stream abgerufen werden."
+      },
+      "errors": {
+        "brandOrCustomUrlRequired": "Wählen Sie entweder einen Kamera-Hersteller mit Host/IP aus oder wählen Sie „Andere“ mit einer benutzerdefinierten URL",
+        "nameRequired": "Kamera-Name benötigt",
+        "nameLength": "Kamera-Name darf höchsten 64 Zeichen lang sein",
+        "invalidCharacters": "Kamera-Name enthält ungültige Zeichen",
+        "nameExists": "Kamera-Name existiert bereits",
+        "brands": {
+          "reolink-rtsp": "Reolink RTSP wird nicht empfohlen. Es wird empfohlen, http in den Kameraeinstellungen zu aktivieren und den Kamera-Assistenten neu zu starten."
+        }
+      },
+      "docs": {
+        "reolink": "https://docs.frigate.video/configuration/camera_specific.html#reolink-cameras"
+      }
+    },
+    "step2": {
+      "description": "Konfigurieren Sie Stream-Rollen und fügen Sie zusätzliche Streams für Ihre Kamera hinzu.",
+      "streamsTitle": "Kamera Streams",
+      "addStream": "Stream hinzufügen",
+      "addAnotherStream": "Weiteren Stream hinzufügen",
+      "streamTitle": "Stream {{nummer}}",
+      "streamUrl": "Stream URL",
+      "streamUrlPlaceholder": "rtsp://nutzername:passwort@host:port/pfad",
+      "url": "URL",
+      "resolution": "Auflösung",
+      "selectResolution": "Auflösung auswählen",
+      "quality": "Qualität",
+      "selectQuality": "Qualität auswählen",
+      "roles": "Rollen",
+      "roleLabels": {
+        "detect": "Objekt-Erkennung",
+        "record": "Aufzeichnung",
+        "audio": "Audio"
+      },
+      "testStream": "Verbindung testen",
+      "testSuccess": "Stream erfolgreich getestet!",
+      "testFailed": "Stream-Test fehlgeschlagen",
+      "testFailedTitle": "Test fehlgeschlagen",
+      "connected": "Verbunden",
+      "notConnected": "Nicht verbunden",
+      "featuresTitle": "Funktionen",
+      "go2rtc": "Verbindungen zur Kamera reduzieren",
+      "detectRoleWarning": "Mindestens ein Stream muss die Rolle „detect“ haben, um fortfahren zu können.",
+      "rolesPopover": {
+        "title": "Stream Rollen",
+        "detect": "Haupt-Feed für Objekt-Erkennung.",
+        "record": "Speichert Segmente des Video-Feeds basierend auf den Konfigurationseinstellungen.",
+        "audio": "Feed für audiobasierte Erkennung."
+      },
+      "featuresPopover": {
+        "title": "Stream Funktionen",
+        "description": "Verwende go2rtc Restreaming, um die Verbindungen zu deiner Kamera zu reduzieren."
+      }
+    },
+    "step3": {
+      "description": "Endgültige Validierung und Analyse vor dem Speichern Ihrer neuen Kamera. Verbinde jeden Stream vor dem Speichern.",
+      "validationTitle": "Stream Validierung",
+      "connectAllStreams": "Verbinde alle Streams",
+      "reconnectionSuccess": "Wiederverbindung erfolgreich.",
+      "reconnectionPartial": "Einige Streams konnten nicht wieder verbunden werden.",
+      "streamUnavailable": "Stream-Vorschau nicht verfügbar",
+      "reload": "Neu laden",
+      "connecting": "Verbinde...",
+      "streamTitle": "Stream {{number}}",
+      "valid": "Gültig",
+      "failed": "Fehlgeschlagen",
+      "notTested": "Nicht getestet",
+      "connectStream": "Verbinden",
+      "connectingStream": "Verbinde",
+      "disconnectStream": "Trennen",
+      "estimatedBandwidth": "Geschätzte Bandbreite",
+      "roles": "Rollen",
+      "none": "Keine",
+      "error": "Fehler",
+      "streamValidated": "Stream {{number}} wurde erfolgreich validiert",
+      "streamValidationFailed": "Stream {{number}} Validierung fehlgeschlagen",
+      "saveAndApply": "Neue Kamera speichern",
+      "saveError": "Ungültige Konfiguration. Bitte prüfe die Einstellungen.",
+      "issues": {
+        "title": "Stream Validierung",
+        "videoCodecGood": "Video-Codec ist {{codec}}.",
+        "audioCodecGood": "Audio-Codec ist {{codec}}.",
+        "noAudioWarning": "Für diesen Stream wurde kein Ton erkannt, die Aufzeichnungen enthalten keinen Ton.",
+        "audioCodecRecordError": "Der AAC-Audio-Codec ist erforderlich, um Audio in Aufnahmen zu unterstützen.",
+        "audioCodecRequired": "Ein Audiostream ist erforderlich, um Audioerkennung zu unterstützen.",
+        "restreamingWarning": "Eine Reduzierung der Verbindungen zur Kamera für den Aufzeichnungsstream kann zu einer etwas höheren CPU-Auslastung führen.",
+        "dahua": {
+          "substreamWarning": "Substream 1 ist auf eine niedrige Auflösung festgelegt. Viele Kameras von Dahua / Amcrest / EmpireTech unterstützen zusätzliche Substreams, die in den Kameraeinstellungen aktiviert werden müssen. Es wird empfohlen, diese Streams zu nutzen, sofern sie verfügbar sind."
+        },
+        "hikvision": {
+          "substreamWarning": "Substream 1 ist auf eine niedrige Auflösung festgelegt. Viele Hikvision-Kameras unterstützen zusätzliche Substreams, die in den Kameraeinstellungen aktiviert werden müssen. Es wird empfohlen, diese Streams zu nutzen, sofern sie verfügbar sind."
+        }
+      }
+    }
+  },
+  "cameraManagement": {
+    "title": "Kameras verwalten",
+    "addCamera": "Neue Kamera hinzufügen",
+    "editCamera": "Kamera bearbeiten:",
+    "selectCamera": "Wähle eine Kamera",
+    "backToSettings": "Zurück zu Kamera-Einstellungen",
+    "streams": {
+      "title": "Kameras aktivieren / deaktivieren",
+      "desc": "Deaktiviere eine Kamera vorübergehend, bis Frigate neu gestartet wird. Deaktivierung einer Kamera stoppt die Verarbeitung der Streams dieser Kamera durch Frigate vollständig. Erkennung, Aufzeichnung und Debugging sind dann nicht mehr verfügbar. <br /> <em>Hinweis: Dies deaktiviert nicht die go2rtc restreams.</em>"
+    },
+    "cameraConfig": {
+      "add": "Kamera hinzufügen",
+      "edit": "Kamera bearbeiten",
+      "description": "Konfiguriere die Kameraeinstellungen, einschließlich Streams und Rollen.",
+      "name": "Kamera-Name",
+      "nameRequired": "Kamera-Name benötigt",
+      "nameLength": "Kamera-Name darf maximal 64 Zeichen lang sein.",
+      "namePlaceholder": "z.B. vordere_tür oder Hof Übersicht",
+      "enabled": "Aktiviert",
+      "ffmpeg": {
+        "inputs": "Eingang Streams",
+        "path": "Stream-Pfad",
+        "pathRequired": "Stream-Pfad benötigt",
+        "pathPlaceholder": "rtsp://...",
+        "roles": "Rollen",
+        "rolesRequired": "Mindestens eine Rolle wird benötigt",
+        "rolesUnique": "Jede Rolle (audio, detect, record) kann nur einem Stream zugewiesen werden",
+        "addInput": "Eingangs-Stream hinzufügen",
+        "removeInput": "Eingangs-Stream entfernen",
+        "inputsRequired": "Es wird mindestens ein Eingangs-Stream benötigt"
+      },
+      "go2rtcStreams": "go2rtc Streams",
+      "streamUrls": "Stream URLs",
+      "addUrl": "URL hinzufügen",
+      "addGo2rtcStream": "go2rtc Stream hinzufügen",
+      "toast": {
+        "success": "Kamera {{cameraName}} erfolgreich gespeichert"
+      }
+    }
+  },
+  "cameraReview": {
+    "title": "Kamera-Einstellungen überprüfen",
+    "object_descriptions": {
+      "title": "Generative KI Objektbeschreibungen",
+      "desc": "Aktiviere/deaktiviere vorübergehend die Objektbeschreibungen durch Generative KI für diese Kamera. Wenn diese Option deaktiviert ist, werden keine KI-generierten Beschreibungen für verfolgte Objekte dieser Kamera erstellt."
+    },
+    "review_descriptions": {
+      "title": "Generative KI Review Beschreibungen",
+      "desc": "Generative KI Review Beschreibungen für diese Kamera vorübergehend aktivieren/deaktivieren. Wenn diese Option deaktiviert ist, werden für die Review Elemente dieser Kamera keine KI-generierten Beschreibungen angefordert."
+    },
+    "review": {
+      "title": "Review",
+      "desc": "Aktivieren/deaktivieren Sie vorübergehend Warnmeldungen und Erkennungen für diese Kamera, bis Frigate neu gestartet wird. Wenn diese Funktion deaktiviert ist, werden keine neuen Überprüfungselemente generiert. ",
+      "alerts": "Warnungen ",
+      "detections": "Erkennungen "
+    },
+    "reviewClassification": {
+      "title": "Bewertungsklassifizierung",
+      "desc": "Frigate kategorisiert zu überprüfende Elemente als Warnmeldungen und Erkennungen. Standardmäßig werden alle Objekte vom Typ <em>person</em> und <em>car</em> als Warnmeldungen betrachtet. Sie können die Kategorisierung der zu überprüfenden Elemente verfeinern, indem Sie die erforderlichen Zonen für sie konfigurieren.",
+      "noDefinedZones": "Für diese Kamera sind keine Zonen definiert.",
+      "objectAlertsTips": "Alle {{alertsLabels}}-Objekte auf {{cameraName}} werden als Warnmeldungen angezeigt.",
+      "zoneObjectAlertsTips": "Alle {{alertsLabels}}-Objekte, die in {{zone}} auf {{cameraName}} erkannt wurden, werden als Warnmeldungen angezeigt.",
+      "objectDetectionsTips": "Alle {{detectionsLabels}}-Objekte, die nicht unter {{cameraName}} kategorisiert sind, werden unabhängig davon, in welcher Zone sie sich befinden, als Erkennungen angezeigt.",
+      "zoneObjectDetectionsTips": {
+        "text": "Alle {{detectionsLabels}}-Objekte, die nicht in {{zone}} auf {{cameraName}} kategorisiert sind, werden als Erkennungen angezeigt.",
+        "notSelectDetections": "Alle {{detectionsLabels}}-Objekte, die in {{zone}} auf {{cameraName}} erkannt und nicht als Warnmeldungen kategorisiert wurden, werden unabhängig davon, in welcher Zone sie sich befinden, als Erkennungen angezeigt.",
+        "regardlessOfZoneObjectDetectionsTips": "Alle {{detectionsLabels}}-Objekte, die nicht unter {{cameraName}} kategorisiert sind, werden unabhängig davon, in welcher Zone sie sich befinden, als Erkennungen angezeigt."
+      },
+      "unsavedChanges": "Nicht gespeicherte Überprüfung der Klassifizierungseinstellungen für {{camera}}",
+      "selectAlertsZones": "Zonen für Warnmeldungen auswählen",
+      "selectDetectionsZones": "Zonen für Erkennungen auswählen",
+      "limitDetections": "Erkennungen auf bestimmte Zonen beschränken",
+      "toast": {
+        "success": "Die Konfiguration der Bewertungsklassifizierung wurde gespeichert. Starten Sie Frigate neu, um die Änderungen zu übernehmen."
+      }
+    }
   }
 }

web/public/locales/el/common.json

@@ -1,10 +1,10 @@
 {
   "time": {
-    "untilForTime": "Ως{{time}}",
+    "untilForTime": "Ως {{time}}",
     "untilForRestart": "Μέχρι να γίνει επανεκίννηση του Frigate.",
     "untilRestart": "Μέχρι να γίνει επανεκκίνηση",
     "justNow": "Μόλις τώρα",
-    "ago": "{{timeAgo}} Πριν",
+    "ago": "Πριν {{timeAgo}}",
     "today": "Σήμερα",
     "yesterday": "Εχθές",
     "last7": "Τελευταίες 7 ημέρες",
@@ -31,7 +31,44 @@
     "lastMonth": "Τελευταίος Μήνας",
     "5minutes": "5 λεπτά",
     "10minutes": "10 λεπτά",
-    "30minutes": "30 λεπτά"
+    "30minutes": "30 λεπτά",
+    "1hour": "1 ώρα",
+    "12hours": "12 ώρες",
+    "24hours": "24 ώρες",
+    "pm": "μ.μ.",
+    "formattedTimestamp": {
+      "12hour": "d MMM, h:mm:ss aaa",
+      "24hour": "d MMM, HH:mm:ss"
+    },
+    "formattedTimestamp2": {
+      "12hour": "MM/dd h:mm:ssa",
+      "24hour": "d MMM HH:mm:ss"
+    },
+    "formattedTimestampHourMinute": {
+      "12hour": "h:mm aaa",
+      "24hour": "HH:mm"
+    },
+    "formattedTimestampHourMinuteSecond": {
+      "12hour": "h:mm:ss aaa",
+      "24hour": "HH:mm:ss"
+    },
+    "formattedTimestampMonthDayHourMinute": {
+      "12hour": "d MMM, h:mm aaa",
+      "24hour": "d MMM, HH:mm"
+    },
+    "formattedTimestampMonthDayYear": {
+      "12hour": "d MMM yyyy",
+      "24hour": "d MMM yyyy"
+    },
+    "formattedTimestampMonthDayYearHourMinute": {
+      "12hour": "d MMM yyyy, h:mm aaa",
+      "24hour": "d MMM yyyy, HH:mm"
+    },
+    "formattedTimestampMonthDay": "d MMM",
+    "formattedTimestampFilename": {
+      "12hour": "dd-MM-yy-h-mm-ss-a",
+      "24hour": "dd-MM-yy-HH-mm-ss"
+    }
   },
   "menu": {
     "live": {
@@ -40,5 +77,49 @@
         "count_other": "{{count}} Κάμερες"
       }
     }
+  },
+  "button": {
+    "save": "Αποθήκευση",
+    "apply": "Εφαρμογή",
+    "reset": "Επαναφορά",
+    "done": "Τέλος",
+    "enabled": "Ενεργοποιημένο",
+    "enable": "Ενεργοποίηση",
+    "disabled": "Απενεργοποιημένο",
+    "disable": "Απενεργοποίηση",
+    "saving": "Αποθήκευση…",
+    "cancel": "Ακύρωση",
+    "close": "Κλείσιμο",
+    "copy": "Αντιγραφή",
+    "back": "Πίσω",
+    "pictureInPicture": "Εικόνα σε εικόνα",
+    "cameraAudio": "Ήχος κάμερας",
+    "edit": "Επεξεργασία",
+    "copyCoordinates": "Αντιγραφή συντεταγμένων",
+    "delete": "Διαγραφή",
+    "yes": "Ναι",
+    "no": "Όχι",
+    "download": "Κατέβασμα",
+    "info": "Πληροφορίες"
+  },
+  "unit": {
+    "speed": {
+      "mph": "mph",
+      "kph": "χλμ/ώρα"
+    },
+    "length": {
+      "meters": "μέτρα"
+    },
+    "data": {
+      "kbps": "kB/s",
+      "mbps": "MB/s",
+      "gbps": "GB/s",
+      "kbph": "kB/ώρα",
+      "mbph": "MB/ώρα",
+      "gbph": "GB/ώρα"
+    }
+  },
+  "label": {
+    "back": "Επιστροφή"
   }
 }

web/public/locales/el/views/classificationModel.json

@@ -0,0 +1 @@
+{}

web/public/locales/el/views/live.json

@@ -62,5 +62,8 @@
   "audioDetect": {
     "enable": "Ενεργοποίηση Ανίχνευσης Ήχου",
     "disable": "Απενεργοποίηση Ανίχνευσης Ήχου"
+  },
+  "noCameras": {
+    "buttonText": "Προσθήκη Κάμερας"
   }
 }

web/public/locales/el/views/settings.json

@@ -42,5 +42,15 @@
   "cameraSetting": {
     "camera": "Κάμερα",
     "noCamera": "Δεν υπάρχει Κάμερα"
+  },
+  "triggers": {
+    "dialog": {
+      "form": {
+        "friendly_name": {
+          "placeholder": "Ονομάτισε ή περιέγραψε αυτό το εύνασμα",
+          "description": "Ένα προαιρετικό φιλικό όνομα, ή ένα περιγραφικό κείμενο για αυτό το εύνασμα."
+        }
+      }
+    }
   }
 }

web/public/locales/en/common.json

@@ -93,7 +93,18 @@
     }
   },
   "label": {
-    "back": "Go back"
+    "back": "Go back",
+    "hide": "Hide {{item}}",
+    "show": "Show {{item}}",
+    "ID": "ID"
+  },
+  "list": {
+    "two": "{{0}} and {{1}}",
+    "many": "{{items}}, and {{last}}"
+  },
+  "field": {
+    "optional": "Optional",
+    "internalID": "The Internal ID Frigate uses in the configuration and database"
   },
   "button": {
     "apply": "Apply",

web/public/locales/en/components/auth.json

@@ -3,6 +3,7 @@
     "user": "Username",
     "password": "Password",
     "login": "Login",
+    "firstTimeLogin": "Trying to log in for the first time? Credentials are printed in the Frigate logs.",
     "errors": {
       "usernameRequired": "Username is required",
       "passwordRequired": "Password is required",

web/public/locales/en/components/dialog.json

@@ -52,7 +52,7 @@
     "export": "Export",
     "selectOrExport": "Select or Export",
     "toast": {
-      "success": "Successfully started export. View the file in the /exports folder.",
+      "success": "Successfully started export. View the file in the exports page.",
       "error": {
         "failed": "Failed to start export: {{error}}",
         "endTimeMustAfterStartTime": "End time must be after start time",
@@ -112,6 +112,7 @@
   },
   "imagePicker": {
     "selectImage": "Select a tracked object's thumbnail",
+    "unknownLabel": "Saved Trigger Image",
     "search": {
       "placeholder": "Search by label or sub label..."
     },

web/public/locales/en/config/face_recognition.json

@@ -23,7 +23,7 @@
       "label": "Min face recognitions for the sub label to be applied to the person object."
     },
     "save_attempts": {
-      "label": "Number of face attempts to save in the train tab."
+      "label": "Number of face attempts to save in the recent recognitions tab."
     },
     "blur_confidence_filter": {
       "label": "Apply blur quality filter to face confidence."

web/public/locales/en/views/classificationModel.json

@@ -1,4 +1,5 @@
 {
+  "documentTitle": "Classification Models",
   "button": {
     "deleteClassificationAttempts": "Delete Classification Images",
     "renameCategory": "Rename Class",
@@ -41,13 +42,96 @@
     "invalidName": "Invalid name. Names can only include letters, numbers, spaces, apostrophes, underscores, and hyphens."
   },
   "train": {
-    "title": "Train",
-    "aria": "Select Train"
+    "title": "Recent Classifications",
+    "titleShort": "Recent",
+    "aria": "Select Recent Classifications"
   },
   "categories": "Classes",
   "createCategory": {
     "new": "Create New Class"
   },
   "categorizeImageAs": "Classify Image As:",
-  "categorizeImage": "Classify Image"
+  "categorizeImage": "Classify Image",
+  "noModels": {
+    "object": {
+      "title": "No Object Classification Models",
+      "description": "Create a custom model to classify detected objects.",
+      "buttonText": "Create Object Model"
+    },
+    "state": {
+      "title": "No State Classification Models",
+      "description": "Create a custom model to monitor and classify state changes in specific camera areas.",
+      "buttonText": "Create State Model"
+    }
+  },
+  "wizard": {
+    "title": "Create New Classification",
+    "steps": {
+      "nameAndDefine": "Name & Define",
+      "stateArea": "State Area",
+      "chooseExamples": "Choose Examples"
+    },
+    "step1": {
+      "description": "State models monitor fixed camera areas for changes (e.g., door open/closed). Object models add classifications to detected objects (e.g., known animals, delivery persons, etc.).",
+      "name": "Name",
+      "namePlaceholder": "Enter model name...",
+      "type": "Type",
+      "typeState": "State",
+      "typeObject": "Object",
+      "objectLabel": "Object Label",
+      "objectLabelPlaceholder": "Select object type...",
+      "classificationType": "Classification Type",
+      "classificationTypeTip": "Learn about classification types",
+      "classificationTypeDesc": "Sub Labels add additional text to the object label (e.g., 'Person: UPS'). Attributes are searchable metadata stored separately in the object metadata.",
+      "classificationSubLabel": "Sub Label",
+      "classificationAttribute": "Attribute",
+      "classes": "Classes",
+      "states": "States",
+      "classesTip": "Learn about classes",
+      "classesStateDesc": "Define the different states your camera area can be in. For example: 'open' and 'closed' for a garage door.",
+      "classesObjectDesc": "Define the different categories to classify detected objects into. For example: 'delivery_person', 'resident', 'stranger' for person classification.",
+      "classPlaceholder": "Enter class name...",
+      "errors": {
+        "nameRequired": "Model name is required",
+        "nameLength": "Model name must be 64 characters or less",
+        "nameOnlyNumbers": "Model name cannot contain only numbers",
+        "classRequired": "At least 1 class is required",
+        "classesUnique": "Class names must be unique",
+        "stateRequiresTwoClasses": "State models require at least 2 classes",
+        "objectLabelRequired": "Please select an object label",
+        "objectTypeRequired": "Please select a classification type"
+      }
+    },
+    "step2": {
+      "description": "Select cameras and define the area to monitor for each camera. The model will classify the state of these areas.",
+      "cameras": "Cameras",
+      "selectCamera": "Select Camera",
+      "noCameras": "Click + to add cameras",
+      "selectCameraPrompt": "Select a camera from the list to define its monitoring area"
+    },
+    "step3": {
+      "selectImagesPrompt": "Select all images with: {{className}}",
+      "selectImagesDescription": "Click on images to select them. Click Continue when you're done with this class.",
+      "generating": {
+        "title": "Generating Sample Images",
+        "description": "Frigate is pulling representative images from your recordings. This may take a moment..."
+      },
+      "training": {
+        "title": "Training Model",
+        "description": "Your model is being trained in the background. Close this dialog, and your model will start running as soon as training is complete."
+      },
+      "retryGenerate": "Retry Generation",
+      "noImages": "No sample images generated",
+      "classifying": "Classifying & Training...",
+      "trainingStarted": "Training started successfully",
+      "errors": {
+        "noCameras": "No cameras configured",
+        "noObjectLabel": "No object label selected",
+        "generateFailed": "Failed to generate examples: {{error}}",
+        "generationFailed": "Generation failed. Please try again.",
+        "classifyFailed": "Failed to classify images: {{error}}"
+      },
+      "generateSuccess": "Successfully generated sample images"
+    }
+  }
 }

web/public/locales/en/views/events.json

@@ -13,11 +13,30 @@
   },
   "timeline": "Timeline",
   "timeline.aria": "Select timeline",
+  "zoomIn": "Zoom In",
+  "zoomOut": "Zoom Out",
   "events": {
     "label": "Events",
     "aria": "Select events",
     "noFoundForTimePeriod": "No events found for this time period."
   },
+  "detail": {
+    "label": "Detail",
+    "noDataFound": "No detail data to review",
+    "aria": "Toggle detail view",
+    "trackedObject_one": "object",
+    "trackedObject_other": "objects",
+    "noObjectDetailData": "No object detail data available.",
+    "settings": "Detail View Settings",
+    "alwaysExpandActive": {
+      "title": "Always expand active",
+      "desc": "Always expand the active review item's object details when available."
+    }
+  },
+  "objectTrack": {
+    "trackedPoint": "Tracked point",
+    "clickToSeek": "Click to seek to this time"
+  },
   "documentTitle": "Review - Frigate",
   "recordings": {
     "documentTitle": "Recordings - Frigate"

web/public/locales/en/views/explore.json

@@ -36,8 +36,8 @@
     "video": "video",
     "object_lifecycle": "object lifecycle"
   },
-  "objectLifecycle": {
-    "title": "Object Lifecycle",
+  "trackingDetails": {
+    "title": "Tracking Details",
     "noImageFound": "No image found for this timestamp.",
     "createObjectMask": "Create Object Mask",
     "adjustAnnotationSettings": "Adjust annotation settings",
@@ -71,7 +71,7 @@
       },
       "offset": {
         "label": "Annotation Offset",
-        "desc": "This data comes from your camera's detect feed but is overlayed on images from the the record feed. It is unlikely that the two streams are perfectly in sync. As a result, the bounding box and the footage will not line up perfectly. However, the <code>annotation_offset</code> field can be used to adjust this.",
+        "desc": "This data comes from your camera's detect feed but is overlayed on images from the the record feed. It is unlikely that the two streams are perfectly in sync. As a result, the bounding box and the footage will not line up perfectly. You can use this setting to offset the annotations forward or backward in time to better align them with the recorded footage.",
         "millisecondsToOffset": "Milliseconds to offset detect annotations by. <em>Default: 0</em>",
         "tips": "TIP: Imagine there is an event clip with a person walking from left to right. If the event timeline bounding box is consistently to the left of the person then the value should be decreased. Similarly, if a person is walking from left to right and the bounding box is consistently ahead of the person then the value should be increased.",
         "toast": {
@@ -168,9 +168,9 @@
       "label": "Download snapshot",
       "aria": "Download snapshot"
     },
-    "viewObjectLifecycle": {
-      "label": "View object lifecycle",
-      "aria": "Show the object lifecycle"
+    "viewTrackingDetails": {
+      "label": "View tracking details",
+      "aria": "Show the tracking details"
     },
     "findSimilar": {
       "label": "Find similar",
@@ -194,12 +194,18 @@
     },
     "deleteTrackedObject": {
       "label": "Delete this tracked object"
+    },
+    "showObjectDetails": {
+      "label": "Show object path"
+    },
+    "hideObjectDetails": {
+      "label": "Hide object path"
     }
   },
   "dialog": {
     "confirmDelete": {
       "title": "Confirm Delete",
-      "desc": "Deleting this tracked object removes the snapshot, any saved embeddings, and any associated object lifecycle entries. Recorded footage of this tracked object in History view will <em>NOT</em> be deleted.<br /><br />Are you sure you want to proceed?"
+      "desc": "Deleting this tracked object removes the snapshot, any saved embeddings, and any associated tracking details entries. Recorded footage of this tracked object in History view will <em>NOT</em> be deleted.<br /><br />Are you sure you want to proceed?"
     }
   },
   "noTrackedObjects": "No Tracked Objects Found",

web/public/locales/en/views/exports.json

@@ -9,6 +9,12 @@
     "desc": "Enter a new name for this export.",
     "saveExport": "Save Export"
   },
+  "tooltip": {
+    "shareExport": "Share export",
+    "downloadVideo": "Download video",
+    "editName": "Edit name",
+    "deleteExport": "Delete export"
+  },
   "toast": {
     "error": {
       "renameExportFailed": "Failed to rename export: {{errorMessage}}"

web/public/locales/en/views/faceLibrary.json

@@ -1,14 +1,10 @@
 {
   "description": {
-    "addFace": "Walk through adding a new collection to the Face Library.",
+    "addFace": "Add a new collection to the Face Library by uploading your first image.",
     "placeholder": "Enter a name for this collection",
     "invalidName": "Invalid name. Names can only include letters, numbers, spaces, apostrophes, underscores, and hyphens."
   },
   "details": {
-    "subLabelScore": "Sub Label Score",
-    "scoreInfo": "The sub label score is the weighted score for all of the recognized face confidences, so this may differ from the score shown on the snapshot.",
-    "face": "Face Details",
-    "faceDesc": "Details of the tracked object that generated this face",
     "timestamp": "Timestamp",
     "unknown": "Unknown"
   },
@@ -19,10 +15,8 @@
   },
   "collections": "Collections",
   "createFaceLibrary": {
-    "title": "Create Collection",
-    "desc": "Create a new collection",
     "new": "Create New Face",
-    "nextSteps": "To build a strong foundation:<li>Use the Train tab to select and train on images for each detected person.</li><li>Focus on straight-on images for best results; avoid training images that capture faces at an angle.</li></ul>"
+    "nextSteps": "To build a strong foundation:<li>Use the Recent Recognitions tab to select and train on images for each detected person.</li><li>Focus on straight-on images for best results; avoid training images that capture faces at an angle.</li></ul>"
   },
   "steps": {
     "faceName": "Enter Face Name",
@@ -33,12 +27,10 @@
     }
   },
   "train": {
-    "title": "Train",
-    "aria": "Select train",
+    "title": "Recent Recognitions",
+    "aria": "Select recent recognitions",
     "empty": "There are no recent face recognition attempts"
   },
-  "selectItem": "Select {{item}}",
-  "selectFace": "Select Face",
   "deleteFaceLibrary": {
     "title": "Delete Name",
     "desc": "Are you sure you want to delete the collection {{name}}? This will permanently delete all associated faces."
@@ -69,7 +61,6 @@
     "maxSize": "Max size: {{size}}MB"
   },
   "nofaces": "No faces available",
-  "pixels": "{{area}}px",
   "trainFaceAs": "Train Face as:",
   "trainFace": "Train Face",
   "toast": {

web/public/locales/en/views/live.json

@@ -175,8 +175,8 @@
     "exitEdit": "Exit Editing"
   },
   "noCameras": {
-    "title": "No Cameras Set Up",
-    "description": "Get started by connecting a camera.",
+    "title": "No Cameras Configured",
+    "description": "Get started by connecting a camera to Frigate.",
     "buttonText": "Add Camera"
   }
 }

web/public/locales/en/views/settings.json

@@ -47,6 +47,10 @@
       "playAlertVideos": {
         "label": "Play Alert Videos",
         "desc": "By default, recent alerts on the Live dashboard play as small looping videos. Disable this option to only show a static image of recent alerts on this device/browser."
+      },
+      "displayCameraNames": {
+        "label": "Always Show Camera Names",
+        "desc": "Always show the camera names in a chip in the multi-camera live view dashboard."
       }
     },
     "storedLayouts": {
@@ -188,6 +192,10 @@
       "testSuccess": "Connection test successful!",
       "testFailed": "Connection test failed. Please check your input and try again.",
       "streamDetails": "Stream Details",
+      "testing": {
+        "probingMetadata": "Probing camera metadata...",
+        "fetchingSnapshot": "Fetching camera snapshot..."
+      },
       "warnings": {
         "noSnapshot": "Unable to fetch a snapshot from the configured stream."
       },
@@ -197,8 +205,9 @@
         "nameLength": "Camera name must be 64 characters or less",
         "invalidCharacters": "Camera name contains invalid characters",
         "nameExists": "Camera name already exists",
+        "customUrlRtspRequired": "Custom URLs must begin with \"rtsp://\". Manual configuration is required for non-RTSP camera streams.",
         "brands": {
-          "reolink-rtsp": "Reolink RTSP is not recommended. It is recommended to enable http in the camera settings and restart the camera wizard."
+          "reolink-rtsp": "Reolink RTSP is not recommended. Enable HTTP in the camera's firmware settings and restart the wizard."
         }
       },
       "docs": {
@@ -272,6 +281,8 @@
         "title": "Stream Validation",
         "videoCodecGood": "Video codec is {{codec}}.",
         "audioCodecGood": "Audio codec is {{codec}}.",
+        "resolutionHigh": "A resolution of {{resolution}} may cause increased resource usage.",
+        "resolutionLow": "A resolution of {{resolution}} may be too low for reliable detection of small objects.",
         "noAudioWarning": "No audio detected for this stream, recordings will not have audio.",
         "audioCodecRecordError": "The AAC audio codec is required to support audio in recordings.",
         "audioCodecRequired": "An audio stream is required to support audio detection.",
@@ -385,7 +396,8 @@
           "mustNotBeSameWithCamera": "Zone name must not be the same as camera name.",
           "alreadyExists": "A zone with this name already exists for this camera.",
           "mustNotContainPeriod": "Zone name must not contain periods.",
-          "hasIllegalCharacter": "Zone name contains illegal characters."
+          "hasIllegalCharacter": "Zone name contains illegal characters.",
+          "mustHaveAtLeastOneLetter": "Zone name must have at least one letter."
         }
       },
       "distance": {
@@ -443,7 +455,7 @@
       "name": {
         "title": "Name",
         "inputPlaceHolder": "Enter a name…",
-        "tips": "Name must be at least 2 characters and must not be the name of a camera or another zone."
+        "tips": "Name must be at least 2 characters, must have at least one letter, and must not be the name of a camera or another zone."
       },
       "inertia": {
         "title": "Inertia",
@@ -875,7 +887,7 @@
       "desc": "Semantic Search must be enabled to use Triggers."
     },
     "management": {
-      "title": "Trigger Management",
+      "title": "Triggers",
       "desc": "Manage triggers for {{camera}}. Use the thumbnail type to trigger on similar thumbnails to your selected tracked object, and the description type to trigger on similar descriptions to text you specify."
     },
     "addTrigger": "Add Trigger",
@@ -895,8 +907,9 @@
       "description": "Description"
     },
     "actions": {
-      "alert": "Mark as Alert",
-      "notification": "Send Notification"
+      "notification": "Send Notification",
+      "sub_label": "Add Sub Label",
+      "attribute": "Add Attribute"
     },
     "dialog": {
       "createTrigger": {
@@ -914,10 +927,11 @@
       "form": {
         "name": {
           "title": "Name",
-          "placeholder": "Enter trigger name",
+          "placeholder": "Name this trigger",
+          "description": "Enter a unique name or description to identify this trigger",
           "error": {
-            "minLength": "Name must be at least 2 characters long.",
-            "invalidCharacters": "Name can only contain letters, numbers, underscores, and hyphens.",
+            "minLength": "Field must be at least 2 characters long.",
+            "invalidCharacters": "Field can only contain letters, numbers, underscores, and hyphens.",
             "alreadyExists": "A trigger with this name already exists for this camera."
           }
         },
@@ -926,18 +940,15 @@
         },
         "type": {
           "title": "Type",
-          "placeholder": "Select trigger type"
-        },
-        "friendly_name": {
-          "title": "Friendly Name",
-          "placeholder": "Name or describe this trigger",
-          "description": "An optional friendly name or descriptive text for this trigger."
+          "placeholder": "Select trigger type",
+          "description": "Trigger when a similar tracked object description is detected",
+          "thumbnail": "Trigger when a similar tracked object thumbnail is detected"
         },
         "content": {
           "title": "Content",
-          "imagePlaceholder": "Select an image",
+          "imagePlaceholder": "Select a thumbnail",
           "textPlaceholder": "Enter text content",
-          "imageDesc": "Select an image to trigger this action when a similar image is detected.",
+          "imageDesc": "Only the most recent 100 thumbnails are displayed. If you can't find your desired thumbnail, please review earlier objects in Explore and set up a trigger from the menu there.",
           "textDesc": "Enter text to trigger this action when a similar tracked object description is detected.",
           "error": {
             "required": "Content is required."
@@ -945,6 +956,7 @@
         },
         "threshold": {
           "title": "Threshold",
+          "desc": "Set the similarity threshold for this trigger. A higher threshold means a closer match is required to fire the trigger.",
           "error": {
             "min": "Threshold must be at least 0",
             "max": "Threshold must be at most 1"
@@ -952,13 +964,30 @@
         },
         "actions": {
           "title": "Actions",
-          "desc": "By default, Frigate fires an MQTT message for all triggers. Choose an additional action to perform when this trigger fires.",
+          "desc": "By default, Frigate fires an MQTT message for all triggers. Sub labels add the trigger name to the object label. Attributes are searchable metadata stored separately in the tracked object metadata.",
           "error": {
             "min": "At least one action must be selected."
           }
         }
       }
     },
+    "wizard": {
+      "title": "Create Trigger",
+      "step1": {
+        "description": "Configure the basic settings for your trigger."
+      },
+      "step2": {
+        "description": "Set up the content that will trigger this action."
+      },
+      "step3": {
+        "description": "Configure the threshold and actions for this trigger."
+      },
+      "steps": {
+        "nameAndType": "Name and Type",
+        "configureData": "Configure Data",
+        "thresholdAndActions": "Threshold and Actions"
+      }
+    },
     "toast": {
       "success": {
         "createTrigger": "Trigger {{name}} created successfully.",

web/public/locales/es/common.json

@@ -280,5 +280,8 @@
     "desc": "Página no encontrada"
   },
   "selectItem": "Seleccionar {{item}}",
-  "readTheDocumentation": "Leer la documentación"
+  "readTheDocumentation": "Leer la documentación",
+  "information": {
+    "pixels": "{{area}}px"
+  }
 }

web/public/locales/es/components/auth.json

@@ -10,6 +10,7 @@
       "loginFailed": "Error de inicio de sesión"
     },
     "password": "Contraseña",
-    "login": "Iniciar sesión"
+    "login": "Iniciar sesión",
+    "firstTimeLogin": "¿Estás tratando de iniciar sesión por primera vez? Las credenciales están impresas en los registros de Frigate."
   }
 }

web/public/locales/es/components/dialog.json

@@ -120,7 +120,8 @@
     "button": {
       "export": "Exportar",
       "markAsReviewed": "Marcar como revisado",
-      "deleteNow": "Eliminar ahora"
+      "deleteNow": "Eliminar ahora",
+      "markAsUnreviewed": "Marcar como no revisado"
     }
   },
   "imagePicker": {

web/public/locales/es/views/classificationModel.json

@@ -0,0 +1 @@
+{}

web/public/locales/es/views/faceLibrary.json

@@ -49,7 +49,7 @@
       "selectImage": "Por favor, selecciona un archivo de imagen."
     },
     "dropActive": "Suelta la imagen aquí…",
-    "dropInstructions": "Arrastra y suelta una imagen aquí, o haz clic para seleccionar",
+    "dropInstructions": "Arrastra y suelta, o pega una imagen aquí, o haz clic para seleccionar",
     "maxSize": "Tamaño máximo: {{size}}MB"
   },
   "toast": {

web/public/locales/es/views/live.json

@@ -147,7 +147,7 @@
     "snapshots": "Capturas de pantalla",
     "autotracking": "Seguimiento automático",
     "cameraEnabled": "Cámara habilitada",
-    "transcription": "Transcripción de audio"
+    "transcription": "Transcripción de Audio"
   },
   "history": {
     "label": "Mostrar grabaciones históricas"
@@ -170,5 +170,10 @@
   "transcription": {
     "enable": "Habilitar transcripción de audio en tiempo real",
     "disable": "Deshabilitar transcripción de audio en tiempo real"
+  },
+  "noCameras": {
+    "title": "No hay cámaras configuradas",
+    "description": "Comienza conectando una cámara.",
+    "buttonText": "Añade Cámara"
   }
 }

web/public/locales/es/views/settings.json

@@ -10,7 +10,9 @@
     "general": "Configuración General - Frigate",
     "frigatePlus": "Configuración de Frigate+ - Frigate",
     "notifications": "Configuración de Notificaciones - Frigate",
-    "enrichments": "Configuración de Análisis Avanzado - Frigate"
+    "enrichments": "Configuración de Análisis Avanzado - Frigate",
+    "cameraManagement": "Administrar Cámaras - Frigate",
+    "cameraReview": "Revisar Configuración de Cámaras - Frigate"
   },
   "menu": {
     "cameras": "Configuración de Cámara",
@@ -23,7 +25,9 @@
     "users": "Usuarios",
     "notifications": "Notificaciones",
     "enrichments": "Análisis avanzado",
-    "triggers": "Disparadores"
+    "triggers": "Disparadores",
+    "roles": "Rols",
+    "cameraManagement": "Administración"
   },
   "dialog": {
     "unsavedChanges": {
@@ -773,7 +777,71 @@
         "desc": "Editar configuractión del disparador para cámara {{camera}}"
       },
       "deleteTrigger": {
-        "title": "Eliminar Disparador"
+        "title": "Eliminar Disparador",
+        "desc": "Está seguro de que desea eliminar el disparador <strong>{{triggerName}}</strong>? Esta acción no se puede deshacer."
+      },
+      "form": {
+        "name": {
+          "title": "Nombre",
+          "placeholder": "Entre nombre de disparador",
+          "error": {
+            "minLength": "El nombre debe tener al menos 2 caracteres.",
+            "invalidCharacters": "El nombre sólo puede contener letras, números, guiones bajos, y guiones.",
+            "alreadyExists": "Un disparador con este nombre ya existe para esta cámara."
+          }
+        },
+        "enabled": {
+          "description": "Activa o desactiva este disparador"
+        },
+        "type": {
+          "title": "Tipo",
+          "placeholder": "Seleccione tipo de disparador"
+        },
+        "friendly_name": {
+          "title": "Nombre amigable",
+          "placeholder": "Nombre o describa este disparador",
+          "description": "Un nombre o texto descriptivo amigable (opcional) para este disparador."
+        },
+        "content": {
+          "title": "Contenido",
+          "imagePlaceholder": "Seleccione una imágen",
+          "textPlaceholder": "Entre contenido de texto",
+          "error": {
+            "required": "El contenido es requrido."
+          },
+          "imageDesc": "Seleccione una imágen para iniciar esta acción cuando una imágen similar es detectada.",
+          "textDesc": "Entre texto para iniciar esta acción cuando la descripción de un objecto seguido similar es detectado."
+        },
+        "threshold": {
+          "title": "Umbral",
+          "error": {
+            "min": "El umbral debe ser al menos 0",
+            "max": "El umbral debe ser al menos 1"
+          }
+        },
+        "actions": {
+          "title": "Acciones",
+          "error": {
+            "min": "Al menos una acción debe ser seleccionada."
+          },
+          "desc": "Por defecto, Frigate manda un mensaje MQTT por todos los disparadores. Seleccione una acción adicional que se realizará cuando este disparador se accione."
+        }
+      }
+    },
+    "semanticSearch": {
+      "title": "Búsqueda semántica desactivada",
+      "desc": "Búsqueda semántica debe estar activada para usar Disparadores."
+    },
+    "toast": {
+      "success": {
+        "createTrigger": "Disparador {{name}} creado exitosamente.",
+        "updateTrigger": "Disparador {{name}} actualizado exitosamente.",
+        "deleteTrigger": "Disparador {{name}} eliminado exitosamente."
+      },
+      "error": {
+        "createTriggerFailed": "Fallo al crear el disparador: {{errorMessage}}",
+        "updateTriggerFailed": "Fallo al actualizar el disparador: {{errorMessage}}",
+        "deleteTriggerFailed": "Fallo al eliminar el disparador: {{errorMessage}}"
       }
     }
   },

web/public/locales/es/views/system.json

@@ -105,7 +105,7 @@
       "unusedStorageInformation": "Información de Almacenamiento No Utilizado"
     },
     "shm": {
-      "title": "Asignación SHM (memoria compartida)",
+      "title": "Asignación de SHM (memoria compartida)",
       "warning": "El tamaño actual de SHM de {{total}}MB es muy pequeño. Aumente al menos a {{min_shm}}MB."
     }
   },

web/public/locales/fa/views/classificationModel.json

@@ -0,0 +1,22 @@
+{
+  "button": {
+    "deleteClassificationAttempts": "حذف تصاویر طبقه بندی",
+    "renameCategory": "تغییر نام کلاس",
+    "deleteCategory": "حذف کردن کلاس",
+    "deleteImages": "حذف کردن عکس ها",
+    "trainModel": "مدل آموزش"
+  },
+  "toast": {
+    "success": {
+      "deletedCategory": "کلاس حذف شده",
+      "deletedImage": "عکس های حذف شده",
+      "categorizedImage": "تصویر طبقه بندی شده",
+      "trainedModel": "مدل آموزش دیده شده.",
+      "trainingModel": "آموزش دادن مدل با موفقیت شروع شد."
+    },
+    "error": {
+      "deleteImageFailed": "حذف نشد:{{پیغام خطا}}",
+      "deleteCategoryFailed": "کلاس حذف نشد:{{پیغام خطا}}"
+    }
+  }
+}