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Josh Hawkins
ed08d4152b
When detect resolution changes mid-session every tracked object we hold was localized against the old pixel grid. Their boxes no longer correspond to anything in the new frame, and the `end` callback that fires when their IDs disappear from the new detect process's detections publishes those stale boxes to consumers (LPR, snapshot crop) that slice the new frame and crash on empty arrays. Drop the tracked-object state on a shape change so no stale boxes ever cross the CameraState boundary. Belt-and-suspenders: also drop any incoming batch whose boxes exceed the current detect resolution. These are in-flight queue entries from the pre-recycle detect process that beat the new detect process to the queue; processing them would re-introduce stale-resolution tracked objects we just dropped above. The per-camera detect process clamps legitimate boxes to detect.width-1 / detect.height-1, so any coord beyond that is unambiguously stale.
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.