Tracking feature points in a video

To start the tracking process, the first thing to do is to detect the feature points in an initial frame. You then try to track these points in the next frame. Obviously, since we are dealing with a video sequence, there is a good chance that the object on which the feature points are found has moved (this motion can also be due to camera movement). Therefore, you must search around a point's previous location in order to find its new location in the next frame. This is what accomplishes the cv::calcOpticalFlowPyrLK function. You input two consecutive frames and a vector of feature points in the first image; the function returns a vector of...