Learning OpenCV

Chapter 10. Tracking and Motion

The Basics of Tracking

When we are dealing with a video source, as opposed to individual still images, we often have a particular object or objects that we would like to follow through the visual field. In the previous chapter, we saw how to isolate a particular shape, such as a person or an automobile, on a frame-by-frame basis. Now what we'd like to do is understand the motion of this object, a task that has two main components: identification and modeling.

Identification amounts to finding the object of interest from one frame in a subsequent frame of the video stream. Techniques such as moments or color histograms from previous chapters will help us identify the object we seek. Tracking things that we have not yet identified is a related problem. Tracking unidentified objects is important when we wish to determine what is interesting based on its motion—or when an object's motion is precisely what makes it interesting. Techniques for tracking unidentified objects typically involve tracking visually significant key points (more soon on what constitutes "significance"), rather than extended objects. OpenCV provides two methods for achieving this: the Lucas-Kanade ^[142] [Lucas81] and Horn-Schunck [Horn81] techniques, which represent what are often referred to as sparse or dense optical flow respectively.

The second component, modeling, helps us address the fact that these techniques are really just providing us with noisy measurement of the object's ...