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* Don't return weighted name if it has the same number of results * Remove link to incorrect format yolov9 models * Fix command list from appearing when other inputs are focused the description box in the tracked object details pane was causing the command input list to show when focused. * clarify face docs * Add note about python yolov9 export * Check if hailort thread is still alive when timeout error is run into * Reduce inference timeout --------- Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com>
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Frigate - NVR With Realtime Object Detection for IP Cameras
[English] | 简体中文
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 GPU or AI accelerator such as a Google Coral or Hailo is highly recommended. AI accelerators will outperform even the best CPUs 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
Live dashboard
Streamlined review workflow
Multi-camera scrubbing
Built-in mask and zone editor
Translations
We use Weblate to support language translations. Contributions are always welcome.
Languages
TypeScript
51.2%
Python
46.3%
CSS
0.8%
Shell
0.6%
Dockerfile
0.5%
Other
0.5%
