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NVR with realtime local object detection for IP cameras
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Kyler e46b020395
Fix: rename docker images and tags in Jetson configuration docs (#10263) 
* rename docker image tags in configuration docs for jetson, add correct image in sample codeblocks, fix typo in 'come'

The image names/tags for Jetson instructions were incorrect. I have updated the docs to point to the correct images.

* revert jetson docs phrasing to properly refer to Jetpack docker images
2024-03-09 15:01:09 +00:00
.devcontainer Docs improvements (#8641) 2023-11-18 08:04:43 -06:00
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docs Fix: rename docker images and tags in Jetson configuration docs (#10263) 2024-03-09 15:01:09 +00:00
frigate Format (#9889) 2024-02-16 09:35:21 -06:00
migrations Performance increase with lots of recordings (#8525) 2023-11-07 23:18:26 +00:00
web Add error handling for unsupported label uploading to frigate+ (#9775) 2024-02-10 13:35:17 -06:00
.dockerignore Improve the devcontainer experience (#3492) 2022-11-20 07:34:12 -06:00
.gitignore Migrate default database path from /media/frigate to /config (#5219) 2023-04-23 11:35:40 -05:00
.pylintrc use fstr log style 2021-02-25 07:01:59 -06:00
audio-labelmap.txt Audio events (#6848) 2023-07-01 08:18:33 -05:00
benchmark_motion.py use a different method for blur and contrast to reduce CPU (#6940) 2023-06-30 07:27:31 -05:00
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CODEOWNERS Initial support for rockchip boards (#8382) 2023-11-02 12:55:24 +00:00
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process_clip.py Improve tracking (#6516) 2023-05-31 08:12:43 -06:00
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README.md Clarify docs about rtmp (#5052) 2023-01-13 07:20:25 -06:00

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

Integration into Home Assistant

Also comes with a builtin UI:

Events