forked from Github/frigate
f9d422b5b5
Setting cache-to=compression=zstd causes the resulting user-pulled image to have zstd-compressed layers, which are not compatible with docker prior to 23.0. Ubuntu 20.04 still ships with docker 20.10, which yields `Error processing tar file` when pulling these images. Renaming the jetpack cache images is my way of clearing the cache of the prior zstd layers, and it clarifies the convention I used for the other cache images in which there is one cache per base image/job, not per target/step. We don't need to delete the non-jetson cache images because they haven't been rebuilt since zstd was enabled.
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:
