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NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
152e585206
* jwt permissions * add old password to body req * add model and migration need to track the datetime that passwords were changed for the jwt * auth api backend changes - use os.open to create jwt secret with restrictive permissions (0o600: read/write for owner only) - add backend validation for password strength - add iat claim to jwt so the server can determine when a token was issued and reject any jwts issued before a user's password_changed_at timestamp, ensuring old tokens are invalidated after a password change - set logout route to public to avoid 401 when logging out - issue new jwt for users who change their own password so they stay logged in * improve set password dialog - add field to verify old password - add password strength requirements * frontend tweaks for password dialog * i18n * use verify endpoint for existing password verification avoid /login side effects (creating a new session) * public logout * only check if password has changed on jwt refresh * fix tests Fix migration 030 by using raw sql to select usernames (avoid ORM selecting nonexistent columns) * add multi device warning to password dialog * remove password verification endpoint Just send old_password + new password in one request, let the backend handle verification in a single operation |
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| config | ||
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| docs | ||
| frigate | ||
| migrations | ||
| notebooks | ||
| web | ||
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| audio-labelmap.txt | ||
| benchmark_motion.py | ||
| benchmark.py | ||
| CODEOWNERS | ||
| cspell.json | ||
| docker-compose.yml | ||
| generate_config_translations.py | ||
| labelmap.txt | ||
| LICENSE | ||
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| netlify.toml | ||
| package-lock.json | ||
| process_clip.py | ||
| pyproject.toml | ||
| 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 LLC 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 © 2025 Frigate LLC.