mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-21 19:07:46 +01:00
NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
741f0a5115
* Bump react-icons from 4.12.0 to 5.0.1 in /web Bumps [react-icons](https://github.com/react-icons/react-icons) from 4.12.0 to 5.0.1. - [Release notes](https://github.com/react-icons/react-icons/releases) - [Commits](https://github.com/react-icons/react-icons/compare/v4.12.0...v5.0.1) --- updated-dependencies: - dependency-name: react-icons dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com> * Update jsdom * Update drawer component * Bump eslint * Update more deps * Fix lint --------- Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> |
||
---|---|---|
.devcontainer | ||
.github | ||
.vscode | ||
config | ||
docker | ||
docs | ||
frigate | ||
migrations | ||
web | ||
.dockerignore | ||
.gitignore | ||
.pylintrc | ||
audio-labelmap.txt | ||
benchmark_motion.py | ||
benchmark.py | ||
CODEOWNERS | ||
docker-compose.yml | ||
labelmap.txt | ||
LICENSE | ||
Makefile | ||
netlify.toml | ||
process_clip.py | ||
pyproject.toml | ||
README.md |
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: