This container takes in a RTSP video feed, use the Google Coral Accelerator to detect a plane and then generates an MQTT message steering the camera to the center of the detected bounding box.

Things to do:

• describe how to config the Axis camera feed. You need to an MJPEG feed. An MP4 based feed has too much latency.
• Need to make this into a container

This repo contains a collection of examples that use camera streams together with the TensorFlow Lite API with a Coral device such as the USB Accelerator or Dev Board and provides an Object tracker for use with the detected objects.

1. First, be sure you have completed the setup instructions for your Coral device. If it's been a while, repeat to be sure you have the latest software.

   Importantly, you should have the latest TensorFlow Lite runtime installed (as per the Python quickstart).

2. Clone this Git repo onto your computer:

   mkdir google-coral && cd google-coral
   
   git clone https://github.com/google-coral/example-object-tracker.git
   
   cd example-object-tracker/
   

3. Download the models:

   sh download_models.sh
   

   These models will be downloaded to a new folder models.

Further requirements may be needed by the different camera libraries, check the README file for the respective subfolder.

• gstreamer: Python examples using gstreamer to obtain camera streem. These examples work on Linux using a webcam, Raspberry Pi with the Raspicam and on the Coral DevBoard using the Coral camera. For the former two you will also need a Coral USB Accelerator to run the models.

  This demo provides the support of an Object tracker. After following the setup instructions in README file for the subfolder gstreamer, you can run the tracker demo:

  cd gstreamer
  python3 detect.py --tracker sort
  

For the demos in this repository you can change the model and the labels file by using the flags flags --model and --labels. Be sure to use the models labeled _edgetpu, as those are compiled for the accelerator - otherwise the model will run on the CPU and be much slower.

For detection you need to select one of the SSD detection models and its corresponding labels file:

mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite, coco_labels.txt