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NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
5239790835
* Initial implementation of active object counters. Need to clean up a bit more and examine reuse of stationary/active logic in neighboring modules. * A bit more cleanup for references to active, referencing the tracked object method rather than duplicating logic. * Minor formatting and readability cleanup * Update docs with the new active mqtt metric definition. * Move the check for a change in active status into the code block protected by a false positive check. * - Add 'active' to the tracked object dictionary, use the previous object for active comparison. - I also missed emitting updates when a tracked object is no longer tracked, and added handling for emitting zeros on object types. |
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frigate | ||
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audio-labelmap.txt | ||
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CODEOWNERS | ||
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LICENSE | ||
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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.