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NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
1133202cbd
* reload the window on 401 * backend apis for auth * add login page * re-enable web linter * fix login page routing * bypass csrf for internal auth endpoint * disable healthcheck in devcontainer target * include login page in vite build * redirect to login page on 401 * implement config for users and settings * implement JWT actual secret * add brute force protection on login * add support for redirecting from auth failures on api calls * return location for redirect * default cookie name should pass regex test * set hash iterations to current OWASP recommendation * move users to database instead of config * config option to reset admin password on startup * user management UI * check for deleted user on refresh * validate username and fixes * remove password constraint * cleanup * fix user check on refresh * web fixes * implement auth via new external port * use x-forwarded-for to rate limit login attempts by ip * implement logout and profile * fixes * lint fixes * add support for user passthru from upstream proxies * add support for specifying a logout url * add documentation * Update docs/docs/configuration/authentication.md Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com> * Update docs/docs/configuration/authentication.md Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com> --------- Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com> |
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| frigate | ||
| migrations | ||
| web | ||
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| .pylintrc | ||
| audio-labelmap.txt | ||
| benchmark_motion.py | ||
| benchmark.py | ||
| CODEOWNERS | ||
| docker-compose.yml | ||
| labelmap.txt | ||
| LICENSE | ||
| Makefile | ||
| netlify.toml | ||
| process_clip.py | ||
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| README.md | ||
Frigate - NVR With Realtime Object Detection for IP Cameras
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 Google Coral Accelerator is optional, but highly recommended. The Coral will outperform even the best CPUs and can process 100+ FPS with very little overhead.
- 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.
Screenshots
Integration into Home Assistant
Also comes with a builtin UI:
