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01452e4c51
* show id field when editing zone * improve zone capitalization * Update NPU models and docs * fix mobilepage in tracked object details * Use thread lock for openvino to avoid concurrent requests with JinaV2 * fix hashing function to avoid collisions * remove extra flex div causing overflow * ensure header stays on top of video controls * don't smart capitalize friendly names * Fix incorrect object classification crop * don't display submit to plus if object doesn't have a snapshot * check for snapshot and clip in actions menu * frigate plus submission fix still show frigate+ section if snapshot has already been submitted and run optimistic update, local state was being overridden * Don't fail to show 0% when showing classification * Don't fail on file system error * Improve title and description for review genai * fix overflowing truncated review item description in detail stream * catch events with review items that start after the first timeline entry review items may start later than events within them, so subtract a padding from the start time in the filter so the start of events are not incorrectly filtered out of the list in the detail stream * also pad on review end_time * fix * change order of timeline zoom buttons on mobile * use grid to ensure genai title does not cause overflow * small tweaks * Cleanup --------- Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
Frigate - NVR With Realtime Object Detection for IP Cameras
[English] | 简体中文
A complete and local NVR designed for Home Assistant with AI object detection. Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras.
Use of a GPU or AI accelerator such as a Google Coral or Hailo is highly recommended. AI accelerators will outperform even the best CPUs with very little overhead.
- Tight integration with Home Assistant via a custom component
- Designed to minimize resource use and maximize performance by only looking for objects when and where it is necessary
- Leverages multiprocessing heavily with an emphasis on realtime over processing every frame
- Uses a very low overhead motion detection to determine where to run object detection
- Object detection with TensorFlow runs in separate processes for maximum FPS
- Communicates over MQTT for easy integration into other systems
- Records video with retention settings based on detected objects
- 24/7 recording
- Re-streaming via RTSP to reduce the number of connections to your camera
- WebRTC & MSE support for low-latency live view
Documentation
View the documentation at https://docs.frigate.video
Donations
If you would like to make a donation to support development, please use Github Sponsors.
Screenshots
Live dashboard
Streamlined review workflow
Multi-camera scrubbing
Built-in mask and zone editor
Translations
We use Weblate to support language translations. Contributions are always welcome.
