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NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
f8b0329b37
* Move database and config from homeassistant /config to addon /config * Re-implement config migration for the add-on * Align some terms * Improve function name * Use local variables * Add model.path migration * Fix homeassistant config path * Ensure migration scripts run before go2rtc and frigate * Migrate all files I know * Add ffmpeg.path migration * Update docker/main/rootfs/etc/s6-overlay/s6-rc.d/prepare/run Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com> * Improve some variable names and organization * Update docs to reflect addon config dir * Update live.md with /addon_configs * Move addon config section to configuration doc * Align several terminologies and improve text * Fix webrtc example config title * Capitalize Add-on in more places * Improve specific add-on config dir docs * Align bash and python scripts to prefer config.yml over config.yaml * Support config.json in migration shell scripts * Change docs to reflect config.yml is preferred over config.yaml * If previous config was yaml, migrate to yaml * Fix typo in edgetpu.md * Fix formatting of Python files * Remove HailoRT Beta add-on variant from docs * Add migration for labelmap and certs * Fix variable name * Fix new_config_file var unset * Fix addon config directories table * Improve db migration to avoid migrating files like .db.bak * Fix echo location --------- Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com> |
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Frigate - NVR With Realtime Object Detection for IP Cameras
EnglishA 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.