So, this new week I started by solving some problems that the tracker had which affected the flow of the application (some more still need to be fixed yet). Also, I had a look on different possible types of tracker implemented in OpenCV (at the moment I am using the TLD). For this purpose I used the website LearnOpenCV of Satya Mallik (https://www.learnopencv.com/object-tracking-using-opencv-cpp-python/) and the OpenCV documentation. I tested all the rest of the trackers mentioned in the post (including the GOTURN which uses deep learning with an offline trained model) but I found that the actual is the better for this purpose.
Furthermore, some bugs were fixed related to the buttons in the GUI and its behavior in the new buffer architecture (as the tracker some more still need to be fixed I guess). I included a new GUI setup with 4 images: the live input video, the combined result from tracker and neural network and the separated results of that two. Now, the images are tagged with the frame number for a better understanding of the application (and some debugging too).
The next image available at the link shows the actual state of the application:
