forked from Github/frigate
9afa1354da
* Limited shm frame count (#12346) * Only keep 2x detect fps frames in SHM * Don't delete previous shm frames in output * Catch case where images do not exist * Ensure files are closed * Clear out all frames when shutting down * Correct the number of frames saved * Simplify empty shm error handling * Improve frame safety * Add handler logs when frame is None * Don't fail on cleanup * Cleanup logging * Update docs * Update calculation * Restore condition * Fix case where thumbnail is saved without frame * Adjust debug logs * Calculate best shm frame count * Fix shm count calculation * Catch missing frame * Formatting * Clarify docs * Catch none frame in autotracking
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.