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NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
b4d4adb75b
* Add field and migration for segment size * Store the segment size in db * Add comment * Add default * Fix size parsing * Include segment size in recordings endpoint * Start adding storage maintainer * Add storage maintainer and calculate average sizes * Update comment * Store segment and hour avg sizes per camera * Formatting * Keep track of total segment and hour averages * Remove unused files * Cleanup 2 hours of recordings at a time * Formatting * Fix bug * Round segment size * Cleanup some comments * Handle case where segments are not deleted on initial run or is only retained segments * Improve cleanup log * Formatting * Fix typo and improve logging * Catch case where no recordings exist for camera * Specifically define sort * Handle edge case for cameras that only record part time * Increase definition of part time recorder * Remove warning about not supported storage based retention * Add note about storage based retention to recording docs * Add tests for storage maintenance calculation and cleanup * Format tests * Don't run for a camera with no recording segments * Get size of file from cache * Rework camera stats to be more efficient * Remove total and other inefficencies * Rewrite storage cleanup logic to be much more efficient * Fix existing tests * Fix bugs from tests * Add another test * Improve logging * Formatting * Set back correct loop time * Update name * Update comment * Only include segments that have a nonzero size * Catch case where camera has 0 nonzero segment durations * Add test to cover zero bandwidth migration case * Fix test * Incorrect boolean logic * Formatting * Explicity re-define iterator |
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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 RTMP to reduce the number of connections to your camera
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:
