<Overview>[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&amp;#xD;
*Uses a very low overhead motion detection to determine where to run object detection&amp;#xD;
*Object detection with Tensorflow runs in a separate process&amp;#xD;
*Object info is published over MQTT for integration into HomeAssistant as a binary sensor&amp;#xD;
*An endpoint is available to view an MJPEG stream for debugging, but should not be used continuously&amp;#xD;
[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.
[span style='color: red;'][b]Note:[/b][/span] You can map an Intel integrated GPU or an Nvidia GPU to use hardware accel. on ffmpeg image encode/decode. In the first place you only need to keep the /dev/dri/ device. If you want to use a nvidia GPU on ffmpeg you need to follow [https://forums.unraid.net/topic/98978-plugin-nvidia-driver/?tab=comments#comment-913250&amp;searchlight=1"]this steps[/a]
[span style='color: red;'][b]Note:[/b][/span] If you are using a PCI Coral instead of a USB one, upgrade your Unraid system to stable 6.9.0 and then install first the needed drivers going to the CA APP and searching for [b]Coral-Driver[/b] (thanks to ich777)
[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.
[span style='color: red;'][b]Note:[/b][/span] If you are using a PCI Coral instead of a USB one, upgrade your Unraid system to stable 6.9.0 and then install first the needed drivers going to the CA APP and searching for [b]Coral-Driver[/b] (thanks to ich777)
<Description>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.
