NVR with realtime local object detection for IP cameras
Go to file
dependabot[bot] 2b21a61e7d
Bump cross-spawn from 7.0.3 to 7.0.6 in /docs
Bumps [cross-spawn](https://github.com/moxystudio/node-cross-spawn) from 7.0.3 to 7.0.6.
- [Changelog](https://github.com/moxystudio/node-cross-spawn/blob/master/CHANGELOG.md)
- [Commits](https://github.com/moxystudio/node-cross-spawn/compare/v7.0.3...v7.0.6)

---
updated-dependencies:
- dependency-name: cross-spawn
  dependency-type: indirect
...

Signed-off-by: dependabot[bot] <support@github.com>
2024-11-18 22:30:25 +00:00
.cspell Add service manager infrastructure (#14150) 2024-10-21 10:00:38 -05:00
.devcontainer Fix devcontainer when there is no ~/.ssh/know_hosts file (#14758) 2024-11-03 08:52:27 -07:00
.github Add docs update to type of change (#14686) 2024-10-30 06:30:00 -06:00
.vscode
config
docker fix tensorrt model generation variable (#14902) 2024-11-10 16:23:32 -06:00
docs Bump cross-spawn from 7.0.3 to 7.0.6 in /docs 2024-11-18 22:30:25 +00:00
frigate Tracked object metadata changes (#15055) 2024-11-18 11:26:44 -07:00
migrations Optimize Explore summary database query (#14797) 2024-11-04 16:04:49 -07:00
notebooks
web Tracked object metadata changes (#15055) 2024-11-18 11:26:44 -07:00
.dockerignore
.gitignore
.pylintrc
audio-labelmap.txt
benchmark_motion.py
benchmark.py
CODEOWNERS
cspell.json
docker-compose.yml Don't generate tensorrt models by default (#14865) 2024-11-08 07:37:18 -06:00
labelmap.txt
LICENSE
Makefile
netlify.toml
package-lock.json
process_clip.py
pyproject.toml
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

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