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729ee86043
* fix stale active object indicators on the live dashboard The camera_activity/<camera> snapshot cache is only written when a client sends onConnect, and object "end" events only update the local state of mounted useCameraActivity hooks, never the cache. As a result, a hook that seeded from a stale cache or missed an "end" event while disconnected showed objects that had already left, with no path to correct itself short of a full page reload. This change will re-request the snapshot on hook mount (collapsed to one onConnect per task across camera cards), and always re-notify camera_activity topics so hooks reconcile against their own local state instead of relying on snapshot-vs-snapshot comparison, and clear the payload dedup cache on reconnect and resync so byte-identical snapshots still apply. * docs tweaks * fix mqtt log message * use consistent values for lpr debug frame filenames with millisecond resolution * apply object events through a functional updater to prevent lost updates The events effect derived a new objects list from the value captured at render time and wrote the whole list back. When events arrived close together, a run derived from a stale list erased a concurrent run's removal; the resurrected object then had no remaining "end" event to clear it, and the add branch could mint a duplicate entry that no splice could ever remove, leaving the live dashboard showing active objects the backend had already cleared, until a page reload. The fix is to apply each event inside setObjects so it operates on the true current list exactly once. Unchanged results return the same reference so React bails out of re-rendering, and the label rewrite is hoisted so added objects get the sub_label/verified label directly instead of relying on the effect re-running against its own state update.
Frigate NVR™ - 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 is highly recommended. AI accelerators will outperform even the best CPUs with very little overhead. See Frigate's supported object detectors.
- 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.
License
This project is licensed under the MIT License.
- Code: The source code, configuration files, and documentation in this repository are available under the MIT License. You are free to use, modify, and distribute the code as long as you include the original copyright notice.
- Trademarks: The "Frigate" name, the "Frigate NVR" brand, and the Frigate logo are trademarks of Frigate, Inc. and are not covered by the MIT License.
Please see our Trademark Policy for details on acceptable use of our brand assets.
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.
Copyright © 2026 Frigate, Inc.