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NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
e2239d36c9
* Auto discover internal WebRTC candidate for add-on * Write logs to stderr * Fix port number * Integrate with newest changes * Update docs * Use local variable more * Use Python to write file, fix JSON->YAML * Store into variable * Update docs/docs/configuration/live.md Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com> * Update docs/docs/configuration/live.md Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com> * Update docs/docs/configuration/live.md * Update docs/docs/configuration/live.md * Refator s6 scripts to the new format * Remove unneeded workaround * Update docker/rootfs/usr/local/go2rtc/create_config.py * Migrate logging to new s6 format * Remove more unnecessary s6 variables * Fix prepare-log and when go2rtc is not present in config * Restart the whole container if either Frigate or go2rtc fails * D * Fix service name in finish * Fix nginx finish comment * Restart improvements * Fix devcontainer * Fix format * Update Dockerfile Co-authored-by: Felipe Santos <felipecassiors@gmail.com> * Improve scripts logging Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com> |
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frigate | ||
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README.md | ||
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requirements-ov.txt | ||
requirements-tensorrt.txt | ||
requirements-wheels.txt | ||
requirements.txt |
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: