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NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
af3f6dadcb
* Ensure viewport is always full screen * Protect against hour with no cards and ensure data is consistent * Reduce grouped up image refreshes * Include current hour and fix scrubbing bugginess * Scroll initially selected timeline in to view * Expand timelne class type * Use poster image for preview on video player instead of using separate image view * Fix available streaming modes * Incrase timing for grouping timline items * Fix audio activity listener * Fix player not switching views correctly * Use player time to convert to timeline time * Update sub labels for previous timeline items * Show mini timeline bar for non selected items * Rewrite desktop timeline to use separate dynamic video player component * Extend improvements to mobile as well * Improve time formatting * Fix scroll * Fix no preview case * Mobile fixes * Audio toggle fixes * More fixes for mobile * Improve scaling of graph motion activity * Add keyboard shortcut hook and support shortcuts for playback page * Fix sizing of dialog * Improve height scaling of dialog * simplify and fix layout system for timeline * Fix timeilne items not working * Implement basic Frigate+ submitting from timeline |
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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: