Mirrors/blakeblackshear.frigate
1
0
Fork 0
mirror of https://github.com/blakeblackshear/frigate.git synced 2024-11-21 19:07:46 +01:00
Code Issues Projects Releases Wiki Activity
NVR with realtime local object detection for IP cameras
634 Commits 60 Branches 126 Tags 151 MiB
Python 47.8%
TypeScript 47.5%
CSS 1.4%
C++ 1.1%
Dockerfile 0.7%
Other 1.5%
a99f360a64
Go to file
Paul Armstrong a99f360a64 refactor(web): use snowpack-plugin-hash
2021-02-20 08:20:17 -06:00
.github Update issue templates 2021-02-10 06:51:52 -06:00
docker pin numpy 2021-01-26 21:40:33 -06:00
docs add camera level ffmpeg params 2021-02-20 08:20:17 -06:00
frigate add camera level ffmpeg params 2021-02-20 08:20:17 -06:00
migrations add database migrations 2021-01-26 21:40:33 -06:00
nginx feat(nginx): enable gzip compression and cache control for static files 2021-01-31 06:18:35 -06:00
web refactor(web): use snowpack-plugin-hash 2021-02-20 08:20:17 -06:00
.dockerignore adding version endpoint 2021-01-26 21:40:33 -06:00
.gitignore feat!: web user interface 2021-01-26 21:40:33 -06:00
benchmark.py support multiple coral devices (fixes #100) 2020-10-18 13:47:13 -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 increment version 2021-02-20 08:20:17 -06:00
README.md clarifying addon docs 2021-01-28 07:45:09 -06:00
run.sh fix graceful exits 2021-01-26 21:40:33 -06:00

README.md

logo

Frigate - NVR With Realtime Object Detection for IP Cameras

A complete and local NVR designed for HomeAssistant 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 HomeAssistant 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 clips of detected objects
  • 24/7 recording
  • Re-streaming via RTMP to reduce the number of connections to your camera

Documentation

View the documentation at https://blakeblackshear.github.io/frigate

Donations

If you would like to make a donation to support development, please use Github Sponsors.

Screenshots

Integration into HomeAssistant

Also comes with a builtin UI:

Events