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NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
c3b313a70d
* Initial audio classification model implementation * fix mypy * Keep audio labelmap local * Cleanup * Start adding config for audio * Add the detector * Add audio detection process keypoints * Build out base config * Load labelmap correctly * Fix config bugs * Start audio process * Fix startup issues * Try to cleanup restarting * Add ffmpeg input args * Get audio detection working * Save event to db * End events if not heard for 30 seconds * Use not heard config * Stop ffmpeg when shutting down * Fixes * End events correctly * Use api instead of event queue to save audio events * Get events working * Close threads when stop event is sent * remove unused * Only start audio process if at least one camera is enabled * Add const for float * Cleanup labelmap * Add audio icon in frontend * Add ability to toggle audio with mqtt * Set initial audio value * Fix audio enabling * Close logpipe * Isort * Formatting * Fix web tests * Fix web tests * Handle cases where args are a string * Remove log * Cleanup process close * Use correct field * Simplify if statement * Use var for localhost * Add audio detectors docs * Add restream docs to mention audio detection * Add full config docs * Fix links to other docs --------- Co-authored-by: Jason Hunter <hunterjm@gmail.com> |
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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:
