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NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
c2e667c0dd
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* face recognition dynamic config * lpr dynamic config * safe changes for birdseye dynamic config * bird classification dynamic config * always assign new config to stats emitter to make telemetry fields dynamic * add wildcard support for camera config updates in config_set * update restart required fields for global sections * add test * fix rebase issue * collapsible settings sidebar use the preexisting control available with shadcn's sidebar (cmd/ctrl-B) to give users more space to set masks/zones on smaller screens * dynamic ffmpeg * ensure previews dir exists when ffmpeg processes restart, there's a brief window where the preview frame generation pipeline is torn down and restarted. before these changes, ffmpeg only restarted on crash/stall recovery or full Frigate restart. Now that ffmpeg restarts happen on-demand via config changes, there's a higher chance a frontend request hits the preview_mp4 or preview_gif endpoints during that brief restart window when the directory might not exist yet. The existing os.listdir() call would throw FileNotFoundError without a directory existence check. this fix just checks if the directory exists and returns 404 if not, exactly how preview_thumbnail already handles the same scenario a few lines below * global ffmpeg section * clean up * tweak * fix test |
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| frigate | ||
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| audio-labelmap.txt | ||
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| benchmark.py | ||
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| README_CN.md | ||
| README.md | ||
| TRADEMARK.md | ||
Frigate NVR™ - Realtime Object Detection for IP Cameras
EnglishA 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.