NVR with realtime local object detection for IP cameras
Go to file
2024-08-08 07:54:13 -06:00
.cspell
.devcontainer Nginx config tweaks (#12174) 2024-06-29 07:18:40 -06:00
.github always release from dev builds 2024-08-08 08:25:19 -05:00
.vscode
config Improve the devcontainer experience (#3492) 2022-11-20 07:34:12 -06:00
docker Build libusb for coral compatibility (#12681) 2024-07-30 16:32:32 -06:00
docs Installation and getting started docs improvements (#12395) 2024-08-03 08:20:14 -05:00
frigate Limit preview threads (#12633) 2024-07-26 09:16:45 -05:00
migrations Auth! (#11347) 2024-05-18 10:36:13 -06:00
notebooks Adds support for YOLO-NAS in OpenVino (#11645) 2024-06-07 05:52:08 -06:00
web Handle case where sub label was null (#12785) 2024-08-08 07:54:13 -06:00
.dockerignore Improve the devcontainer experience (#3492) 2022-11-20 07:34:12 -06:00
.gitignore upgrade to latest openvino version (#11563) 2024-05-27 14:49:35 -06:00
.pylintrc
audio-labelmap.txt Audio events (#6848) 2023-07-01 08:18:33 -05:00
benchmark_motion.py
benchmark.py
CODEOWNERS
cspell.json
docker-compose.yml
labelmap.txt
LICENSE
Makefile Update version 2024-08-08 07:54:13 -06:00
netlify.toml
process_clip.py
pyproject.toml
README.md update images in readme 2024-06-08 15:37:16 -05:00

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

Live dashboard

Live dashboard

Streamlined review workflow

Streamlined review workflow

Multi-camera scrubbing

Multi-camera scrubbing

Built-in mask and zone editor

Multi-camera scrubbing