Feature-based tracking refers to tracking individual feature points across successive frames in the video. The advantage here is that we don't have to detect feature points in every single frame. We can just detect them once and keep tracking them after that. This is more efficient as compared to running the detector on every frame. We use a technique called optical flow to track these features. Optical flow is one of the most popular techniques in Computer Vision. We choose a bunch of feature points, and track them through the video stream. When we detect the feature points, we compute the displacement vectors and show the motion of those keypoints between consecutive frames. These vectors are called motion vectors.
A motion vector for a particular point is just a directional line that indicates where that point has moved as compared to the previous frame. Different methods are used to detect these motion vectors. The two most popular algorithms are the Lucas-Kanade...