I spent this week working on our person detection system using visual light as a backup to using thermal light (thermal camera and algorithms that detect if there are people in thermal images). This meant connecting the two visual cameras that we got this week from the ECE inventory to our Raspberry Pi, and working on adjusting our detection algorithms to work on visual data instead of thermal data, and making sure that it would successful detect people in the environment in which it would be deployed. I was able to get the first camera connected, however that camera might be having some problems because while it did capture pictures (and those images did somewhat respond to what the camera should be seeing, for instance if you covered the camera, the image it would pick up would be all dark whereas if you didn’t, then the image would be light) those pictures didn’t really represent anything at all; basically the images it would pick up were just an entire screen of blue pixels. I’ve been working on getting the other camera we were lent (the webcam, which should connect fairly simply by USB) set up with our Raspberry Pi, but I have not finished doing so. I also worked on our visual data detection. On Monday, Jeremy allowed me to take some pictures of him in the lab (from approximately the point of view of what the robot would be seeing, i.e. from the ground), and I’ve been using these images to test if our detection is working or not. Currently I’m trying to do the visual detection with HOG, however so far this is not working; if I can’t get it to work with HOG, then more advanced methods might be necessary (CNNs). Next week, our new FLIR breakout board is expected to arrive, so I hope to have this subsystem done either using visual or thermal data. Either way, I expect to be able to get a camera set up and working with our Pi and a detection algorithm tuned to the robots environment working.
As I’ve worked on this project, I’ve mostly picked up new knowledge in computer vision techniques for purposes of our person detection needs (for instance, background subtraction, edge detection, HOG algorithms, and CNNs), and videos on YouTube as well as course content from the CMU course on computer vision has been very helpful for this. In particular, on a subject like this, I found videos to be very helpful in showing visually how each of these algorithms/techniques work. I’ve also searched online for information on OpenCV and what features are available in that library.