NVR with realtime local object detection for IP cameras
Go to file
dependabot[bot] 0e981c35f9
Bump braces from 3.0.2 to 3.0.3 in /docs
Bumps [braces](https://github.com/micromatch/braces) from 3.0.2 to 3.0.3.
- [Changelog](https://github.com/micromatch/braces/blob/master/CHANGELOG.md)
- [Commits](https://github.com/micromatch/braces/compare/3.0.2...3.0.3)

---
updated-dependencies:
- dependency-name: braces
  dependency-type: indirect
...

Signed-off-by: dependabot[bot] <support@github.com>
2024-06-16 09:42:08 +00:00
.cspell
.devcontainer
.github Update template to include all detector types (#11981) 2024-06-15 15:26:09 -05:00
.vscode
config
docker Update FFmpeg for Rockchip (#11952) 2024-06-14 07:25:33 -05:00
docs Bump braces from 3.0.2 to 3.0.3 in /docs 2024-06-16 09:42:08 +00:00
frigate set shortest edge to preview height (#11971) 2024-06-15 06:42:23 -05:00
migrations
notebooks Adds support for YOLO-NAS in OpenVino (#11645) 2024-06-07 05:52:08 -06:00
web Optional proxy in config (#11973) 2024-06-15 08:01:19 -05:00
.dockerignore
.gitignore upgrade to latest openvino version (#11563) 2024-05-27 14:49:35 -06:00
.pylintrc
audio-labelmap.txt
benchmark_motion.py
benchmark.py
CODEOWNERS
cspell.json
docker-compose.yml
labelmap.txt
LICENSE
Makefile
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