mirror of
https://github.com/blakeblackshear/frigate.git
synced 2025-09-26 19:41:29 +08:00
77589c18f4cc685600c19f2b08dfab6ce4e31c80

* Update reverse_proxy.md Adds a small, working, config for Caddy * Update docs/docs/guides/reverse_proxy.md Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com> * Update docs/docs/guides/reverse_proxy.md Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com> --------- Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
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
Streamlined review workflow
Multi-camera scrubbing
Built-in mask and zone editor
Description
NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
Readme
MIT
301 MiB
Languages
TypeScript
49%
Python
48.7%
CSS
0.7%
Shell
0.6%
Dockerfile
0.4%
Other
0.4%