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* don't calibrate or skip capability checks when autotracking is enabled at runtime Enabling autotracking from the UI on a camera that had been disabled for missing FOV relative movement or MoveStatus skipped both checks because ONVIF was already initialized, then ran calibration on the object processing thread and hung waiting for a motor stop that never came. Setup now runs the capability checks every time, only calibrates at startup, stops tracking the triggering object when setup disables the camera, and publishes the OFF state so the toggle reflects it. * only show settings reset buttons when the reset can succeed Reset to Global and Reset to Default delete the section from config.yml, and the delete raised a KeyError (HTTP 500) when the key wasn't in the file. Camera live sections always read as overridden because the backend fills in live.streams per camera, and the global Reset to Default button showed for every section, including ones the user never wrote. Global reset now only shows when the section differs from its schema defaults, ignoring fields the backend resolves at runtime (ffmpeg and record export hwaccel_args, audio filters). Camera live overrides skip streams, ffmpeg overrides skip inputs, and reset is hidden for camera ffmpeg and detection models, where deleting the section can't produce a valid config. Birdseye modes uses a plain default so the schema carries it. * Added an e2e spec covering reset visibility for default and customized global sections, the runtime-resolved fields, camera live overrides, and camera ffmpeg
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.