Mirrors/blakeblackshear.frigate
1
0
Fork 0
mirror of https://github.com/blakeblackshear/frigate.git synced 2025-02-18 00:16:41 +01:00
Code Issues Projects Releases Wiki Activity
NVR with realtime local object detection for IP cameras
2,164 Commits 48 Branches 134 Tags 252 MiB
TypeScript 49.3%
Python 46.6%
CSS 1.1%
C++ 0.8%
Shell 0.7%
Other 1.3%
1eb5105b24
Go to file
Marc Altmann 1eb5105b24
Adding go2rtc presets for Rockchip hardware acceleration (#8686) 
* add go2rtc presets for rk hw accel

* code formatting

* add docs about go2rtc presets

* Update comment
2023-11-20 19:10:44 -06:00
.devcontainer Docs improvements (#8641) 2023-11-18 08:04:43 -06:00
.github
.vscode
config
docker Adding go2rtc presets for Rockchip hardware acceleration (#8686) 2023-11-20 19:10:44 -06:00
docs Adding go2rtc presets for Rockchip hardware acceleration (#8686) 2023-11-20 19:10:44 -06:00
frigate Customize regions grid overlay API (#8668) 2023-11-19 14:51:54 +00:00
migrations Performance increase with lots of recordings (#8525) 2023-11-07 23:18:26 +00:00
web update web deps (#8663) 2023-11-19 05:24:52 -06:00
.dockerignore
.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
benchmark_motion.py use a different method for blur and contrast to reduce CPU (#6940) 2023-06-30 07:27:31 -05:00
benchmark.py
CODEOWNERS
docker-compose.yml
labelmap.txt
LICENSE
Makefile Community Supported Boards Framework (#7114) 2023-07-23 16:45:29 -05:00
netlify.toml
process_clip.py
pyproject.toml Docs improvements (#8641) 2023-11-18 08:04:43 -06:00
README.md

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 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