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
2,314 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%
7d18c2c03d
Go to file
Josh Hawkins 7d18c2c03d
UI tweaks (#9996) 
* various tweaks

* update debounce time

* scroll to top with new events
2024-02-23 06:52:54 -07:00
.devcontainer Remove deprecated RTMP port 1935 (#9137) 2024-01-31 12:56:11 +00:00
.github another name fix (#9886) 2024-02-16 06:51:19 -06:00
.vscode
config
docker Update FFmpeg for Rockchip image (#9912) 2024-02-20 23:21:24 +00:00
docs fix typos (#9895) 2024-02-17 16:01:50 -06:00
frigate Setting motion masks to zero, rather than 255, will speed up calibration, as the average frame starts out at zero (#9992) 2024-02-23 06:08:05 -06:00
migrations Create ReviewSegment table in DB for organizing detections to be reviewed (#9918) 2024-02-20 16:26:09 -07:00
web UI tweaks (#9996) 2024-02-23 06:52:54 -07:00
web-old Merge remote-tracking branch 'origin/master' into dev 2024-02-14 18:20:55 -06:00
.dockerignore
.gitignore Small autotracking changes (#9571) 2024-02-02 06:23:14 -06:00
.pylintrc use fstr log style 2021-02-25 07:01:59 -06:00
audio-labelmap.txt
benchmark_motion.py
benchmark.py
CODEOWNERS
docker-compose.yml
labelmap.txt
LICENSE
Makefile increment version 2024-01-31 06:23:54 -06:00
netlify.toml
process_clip.py
pyproject.toml
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