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NVR with realtime local object detection for IP cameras
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Nicolas Mowen b7b7e1b78b Face recognition backend (#14495) 
* Add basic config and face recognition table

* Reconfigure updates processing to handle face

* Crop frame to face box

* Implement face embedding calculation

* Get matching face embeddings

* Add support face recognition based on existing faces

* Use arcface face embeddings instead of generic embeddings model

* Add apis for managing faces

* Implement face uploading API

* Build out more APIs

* Add min area config

* Handle larger images

* Add more debug logs

* fix calculation

* Reduce timeout

* Small tweaks

* Use webp images

* Use facenet model
2024-11-16 07:49:14 -07:00
.cspell Add service manager infrastructure (#14150) 2024-10-21 10:00:38 -05:00
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.github Add docs update to type of change (#14686) 2024-10-30 06:30:00 -06:00
.vscode Fix vscode launch configuration (#13795) 2024-09-17 10:42:10 -05:00
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docker Face recognition backend (#14495) 2024-11-16 07:49:14 -07:00
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frigate Face recognition backend (#14495) 2024-11-16 07:49:14 -07:00
migrations Optimize Explore summary database query (#14797) 2024-11-04 16:04:49 -07:00
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README.md

README.md

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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

Live dashboard

Live dashboard

Streamlined review workflow

Streamlined review workflow

Multi-camera scrubbing

Multi-camera scrubbing

Built-in mask and zone editor

Multi-camera scrubbing