Accomplishments
This week, I worked more on the camera integration, working to produce an output that would be suitable for the kalman filter to run on. I integrated the depth sensor with the object detection model, so now it is able to generate a X, Y, Z coordinate position of the ball as it is being detected and tracked in real time. I also worked on improving the accuracy of the kalman filter, looking at multiple different implementations using OpenCV, and also read some papers to conceptually understand the background knowledge in the case that I would need to write my own filter. Finally, to help with the camera vision pipeline, I also added a background subtraction filter to allow more ease for the yolo model to track fast moving objects. This gave good results, as now putting video playback onto the detection model, it was able to detect the ball in every single frame of the video.Â
Schedule and deliverables
With a lot of work being done in the camera pipeline, I am feeling good about being on schedule overall. However, the kalman filter still remains my biggest concern, as it is difficult to get a working implementation without any good documentation or background. My main goal next week is to get the new implementation of the kalman filter working and test out a proof of concept. It will be my top priority as the results from this POC will determine whether we would need update our design for the camera location and camera angle.