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NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
43ade86796
* Add support for ptz commands via websocket * Fix startup issues * Fix bugs * Set config manually * Add more commands * Add presets * Add zooming * Fixes * Set name * Cleanup * Add ability to set presets from UI * Add ability to set preset from UI * Cleanup for errors * Ui tweaks * Add visual design for pan / tilt * Add pan/tilt support * Support zooming * Try to set wsdl * Fix duplicate logs * Catch auth errors * Don't init onvif for disabled cameras * Fix layout sizing * Don't comment out * Fix formatting * Add ability to control camera with keyboard shortcuts * Disallow user selection * Fix mobile pressing * Remove logs * Substitute onvif password * Add ptz controls ot birdseye * Put wsdl back * Add padding * Formatting * Catch onvif error * Optimize layout for mobile and web * Place ptz controls next to birdseye view in large layout * Fix pt support * Center text titles * Update tests * Update docs * Write camera docs for PTZ * Add MQTT docs for PTZ * Add ptz info docs for http * Fix test * Make half width when full screen * Fix preset panel logic * Fix parsing * Update mqtt.md * Catch preset error * Add onvif example to docs * Remove template example from main camera docs |
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config | ||
docker | ||
docs | ||
frigate | ||
migrations | ||
web | ||
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.pylintrc | ||
benchmark.py | ||
docker-compose.yml | ||
Dockerfile | ||
labelmap.txt | ||
LICENSE | ||
Makefile | ||
process_clip.py | ||
README.md | ||
requirements-dev.txt | ||
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: