The blob models and the contour analyzer produce many of the same features (head, hands, feet), but with very different failure modes. The contour analysis can find the features in a single frame if they exist, but the results tend to be noisy. The class analysis produces accurate results, and can track the features where the contour can not, but it depends on the stability of the underlying models and the continuity of the underlying features (i.e., no occlusion).
The last stage of model building involves the reconciliation of these two modes. For each feature, Pfinder heuristically rates the validity of the signal from each mode. The signals are then blended with prior probabilities derived from these ratings. This allows the color trackers to track the hands in front of the body--when the hands produce no evidence in the contour. If the class models become lost due to occlusion or rapid motion, the contour tracker will dominate and will set the feature positions once they are re-acquired in the contour.