frigate blakeblackshear/frigate:stable-amd64 https://hub.docker.com/r/blakeblackshear/frigate/ bridge sh false https://forums.unraid.net/topic/98064-support-blakeblackshear-frigate/ https://github.com/blakeblackshear/frigate [b]NVR With Realtime Object Detection for IP Cameras[/b] Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras. Designed for integration with HomeAssistant or others via MQTT. Use of a Google Coral Accelerator is optional, but highly recommended. On my Intel i7 processor, I can process 2-3 FPS with the CPU. The Coral can process 100+ FPS with very low CPU load. *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 a separate process *Object info is published over MQTT for integration into HomeAssistant as a binary sensor *An endpoint is available to view an MJPEG stream for debugging, but should not be used continuously [span style='color: red;'][b]Note:[/b][/span] A config.yml file [b]must exist[/b] in the config directory. See example [a href="https://github.com/blakeblackshear/frigate/blob/master/config/config.example.yml"]here[/a] and device specific info can be found [a href="https://github.com/blakeblackshear/frigate/blob/master/docs/DEVICES.md"]here[/a]. See the documentation for more details. http://[IP]:[PORT:5000] https://raw.githubusercontent.com/yayitazale/unraid-templates/main/yayitazale/frigate-amd64.xml https://raw.githubusercontent.com/yayitazale/unraid-templates/main/frigate_unraid.PNG --shm-size=512m bridge 5000 5000 tcp /mnt/user/appdata/frigate /config rw /etc/localtime /etc/localtime rw /mnt/user/Media/frigate/clips /clips rw /cache /tmpfs rw enterpassword FRIGATE_RTSP_PASSWORD /dev/bus/usb /mnt/user/appdata/frigate /etc/localtime 5000 enterpassword /mnt/user/Media/frigate/clips /cache /dev/dri/ 1607672518 HomeAutomation: Security: NVR With Realtime Object Detection for IP Cameras Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras. Designed for integration with HomeAssistant or others via MQTT. Use of a Google Coral Accelerator is optional, but highly recommended. On my Intel i7 processor, I can process 2-3 FPS with the CPU. The Coral can process 100+ FPS with very low CPU load. *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 a separate process *Object info is published over MQTT for integration into HomeAssistant as a binary sensor *An endpoint is available to view an MJPEG stream for debugging, but should not be used continuously /tmp/GitHub/AppFeed/templates/yayitazalesRepository/yayitazale/frigate-amd64.xml