Feature extraction: We will describe an object of interest with Speeded-Up Robust Features (SURF), which is an algorithm used to find distinctive keypoints in an image that are both scale invariant and rotation invariant. These keypoints will help us make sure that we are tracking the right object over multiple frames. Because the appearance of the object might change from time to time, it is important to find keypoints that do not depend on the viewing distance or viewing angle of the object (hence the scale and rotation invariance).

Feature matching: We will try to establish a correspondence between keypoints using the Fast Library for Approximate Nearest Neighbors (FLANN) to see whether a frame contains keypoints similar to the keypoints from our object of interest. If we find a good match, we will mark the object in each frame.

Feature tracking: We will keep track of the located object...