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* Basic functionality * Threaded motion estimator * Revert "Threaded motion estimator" This reverts commit 3171801607cbbbe58cda7b637f2a3917d919959a. * Don't detect motion when ptz is moving * fix motion logic * fix mypy error * Add threaded queue for movement for slower ptzs * Move queues per camera * Move autotracker start to app.py * iou value for tracked object * mqtt callback * tracked object should be initially motionless * only draw thicker box if autotracking is enabled * Init if enabled when initially disabled in config * Fix init * Thread names * Always use motion estimator * docs * clarify fov support * remove size ratio * use mp event instead of value for ptz status * update autotrack at half fps * fix merge conflict * fix event type for mypy * clean up * Clean up * remove unused code * merge conflict fix * docs: update link to object_detectors page * Update docs/docs/configuration/autotracking.md Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com> * clarify wording * pass actual instances directly * default return preset * fix type * Error message when onvif init fails * disable autotracking if onvif init fails * disable autotracking if onvif init fails * ptz module * verify required_zones in config * update util after dev merge --------- Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
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:
