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NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
* fix i18n keys * hide disable from context menu for viewers * Fix auto live check for default dashboard and camera groups Disabling the Automatic Live View switch in Settings should prevent streaming from occurring. Overriding any settings in a camera group will override the global setting. The check here incorrectly always returned false instead of undefined. * clarify hardware accelerated enrichments * clarify * add note about detect stream to face rec docs * add note about low end Dahuas for autotracking * Catch invalid face box / image * Video tab tweaks With the changes in https://github.com/blakeblackshear/frigate/pull/18220, the video tab in the Tracked Object Details pane now correctly trims the in-browser HLS video. Because of keyframes and record/detect stream differences, we can manually subtract a couple of seconds from the event start_time to ensure the first few frames aren't cut off from the video * Clarify * Don't use Migraphx by default * Provide better support for running embeddings on GPU * correctly join cameras * Adjust blur confidence reduction --------- Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com> |
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README_CN.md | ||
README.md |
Frigate - NVR With Realtime Object Detection for IP Cameras
English
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.