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NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
6e0faa930a
When home assistant mate the default values change last year it broke the original blueprint when it looked for the camera fps as no default was coded. I've been maintaining a fixed version with new features in hunterjms absence. Yesterday the mods split the thread so this is the latest post and blueprint now. |
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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: