Tracking feature points in a video
This chapter is about reading, writing, and processing video sequences. The objective is to be able to analyze a complete video sequence. As an example, in this recipe, you will learn how to perform temporal analysis of the sequence in order to track feature points as they move from frame to frame.
How to do it...
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 new...
