Mirrors/blakeblackshear.frigate
1
0
Fork 0
mirror of https://github.com/blakeblackshear/frigate.git synced 2024-12-19 19:06:16 +01:00
Code Issues Projects Releases Wiki Activity
NVR with realtime local object detection for IP cameras
1,116 Commits 62 Branches 127 Tags 239 MiB
Python 47.8%
TypeScript 47.5%
CSS 1.4%
C++ 1.1%
Dockerfile 0.7%
Other 1.5%
4ab0927de8
Go to file
Blake Blackshear 4ab0927de8 no need to expire recordings every minute
2022-02-18 21:18:26 -06:00
.devcontainer updated devcontainer 2021-06-12 07:23:14 -05:00
.github Merge pull request #825 from blakeblackshear/release-0.9.0 2021-10-05 17:59:25 -05:00
docker add support for rockchip hwaccel 2021-09-25 08:25:00 -05:00
docs fix default motion comment 2022-02-18 21:18:26 -06:00
frigate no need to expire recordings every minute 2022-02-18 21:18:26 -06:00
migrations only save recordings when an event is in progress 2022-02-18 21:18:26 -06:00
web use snapshot url to support in progress events 2022-02-18 21:18:26 -06:00
.dockerignore update ignore files 2021-10-23 08:21:15 -05:00
.gitignore revamp process clip 2022-02-18 21:18:26 -06:00
.pylintrc use fstr log style 2021-02-25 07:01:59 -06:00
benchmark.py support multiple coral devices (fixes #100) 2020-10-18 13:47:13 -05:00
docker-compose.yml update birdseye layout calculations 2021-06-12 07:23:14 -05:00
labelmap.txt refactor and reduce false positives 2020-09-17 07:37:27 -05:00
LICENSE switch to MIT license 2020-07-26 12:07:47 -05:00
Makefile version tick 2022-02-18 21:18:26 -06:00
process_clip.py use resolution of clip 2022-02-18 21:18:26 -06:00
README.md update docs url 2021-09-26 16:43:26 -05:00

README.md

logo

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 RTMP to reduce the number of connections to your camera

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