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NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
b359ff1b8e
* added timepicker as children to calendar * new timepicker component * Add timepicker * new timePicker component * timepicker as calendar child * hover:border and rounded * adjusted width * complete rework * more code comments * memorization * preselect hover, transition * numberOfDaysSelected has minimum of 1 * prefill hours when component mounts * persist hours when component mount * accommodate for the new timePicker * add reset state * scroll into view * reuse before, after * fix LastDayInRange when a time is selected * do not add hours if before is zero * use hours instead of days * useeffect to reset hour. check timerange before scroll * scroll to last element, not first |
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config | ||
docker | ||
docs | ||
frigate | ||
migrations | ||
web | ||
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.pylintrc | ||
benchmark_motion.py | ||
benchmark.py | ||
docker-compose.yml | ||
Dockerfile | ||
labelmap.txt | ||
LICENSE | ||
Makefile | ||
process_clip.py | ||
pyproject.toml | ||
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: