This chapter presents a framework for human motion understanding, defined as estimation of the physical state of the body combined with interpretation of that part of the motion that cannot be predicted by passive physics alone. The behavior system operates in conjunction with a real-time, fully-dynamic, 3-D person tracking system that provides a mathematically concise formulation for incorporating a wide variety of physical constraints and probabilistic influences. The framework takes the form of a non-linear recursive filter that enables even pixel-level processes to take advantage of the contextual knowledge encoded in the higher-level models. Some of the demonstrated benefits of this approach include: increase in 3-D tracking accuracy, insensitivity to temporary occlusion, and the ability to handle multiple people.
The intimate integration of the behavior system and the dynamic model also provides the opportunity for a richer sort of motion understanding. The innovations are one step closer to the original intent, our statistical models don't have to disentangle the message from the means of expression.
The next chapter will describe a game interface built around this technology. This domain will provide opportunities to apply these techniques to a richer domain of movement.