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Josh Hawkins
4c3e0fdea9
TrackedObjectProcessor drained all pending camera config updates at once but handled them in a mutually exclusive if/elif on enabled/add/remove, so only one topic was processed per drain. When an add arrived in the same batch as an enabled update, the add was skipped and the new camera never got a camera state. Adding a camera reliably produced that batch: config_set now re-applies runtime overrides, which republishes an enabled update for every previously toggled camera immediately before the add, in the same request. The dashboard and camera capture still saw the camera (the maintainer does not subscribe to enabled, so it got a clean add-only batch), but object_processing did not, and disabling the camera then crashed with a KeyError on the unguarded camera_states lookup. Handle add and remove independently instead of as exclusive branches so a batched add is no longer dropped, and guard the remove lookup so a missing state is skipped rather than raising. Drop the enabled branch entirely: it only ever set prev_enabled when it was None, but prev_enabled is seeded to a bool at camera state creation and is never None (mypy flags the body as unreachable), and the actual enable/disable transition is already driven by the disabled-state loop from config.enabled.
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.