Learning OpenCV

Motion Templates

Motion templates were invented in the MIT Media Lab by Bobick and Davis [Bobick96; Davis97] and were further developed jointly with one of the authors [Davis99; Bradski00]. This more recent work forms the basis for the implementation in OpenCV.

Motion templates are an effective way to track general movement and are especially applicable to gesture recognition. Using motion templates requires a silhouette (or part of a silhouette) of an object. Object silhouettes can be obtained in a number of ways.

1. The simplest method of obtaining object silhouettes is to use a reasonably stationary camera and then employ frame-to-frame differencing (as discussed in Chapter 9). This will give you the moving edges of objects, which is enough to make motion templates work.

2. You can use chroma keying. For example, if you have a known background color such as bright green, you can simply take as foreground anything that is not bright green.

3. Another way (also discussed in Chapter 9) is to learn a background model from which you can isolate new foreground objects/people as silhouettes.

4. You can use active silhouetting techniques—for example, creating a wall of near-infrared light and having a near-infrared-sensitive camera look at the wall. Any intervening object will show up as a silhouette.

5. You can use thermal imagers; then any hot object (such as a face) can be taken as foreground.

6. Finally, you can generate silhouettes by using the segmentation techniques (e.g., pyramid segmentation or mean-shift ...