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NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
f48dd8c1ab
* Add functionality to update YAML config file with PUT request in HTTP endpoint * Refactor copying of text to clipboard with Clipboard API and fallback to document.execCommand('copy') in CameraMap.jsx file * Update YAML file from URL query parameters in frigate/http.py and add functionality to save motion masks, zones, and object masks in CameraMap.jsx * formatting * fix merging fuckup * Refactor camera zone coordinate saving to use single query parameter per zone in CameraMap.jsx * remove unnecessary print statements in util.py * Refactor update_yaml_file function to separate the logic for updating YAML data into a new function update_yaml(). * fix merge errors * Refactor code to improve error handling and add dependencies to useEffect hooks |
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.github | ||
.vscode | ||
config | ||
docker | ||
docs | ||
frigate | ||
migrations | ||
web | ||
.dockerignore | ||
.gitignore | ||
.pylintrc | ||
audio-labelmap.txt | ||
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: