Real-time human motion sensing based on vision-based inverse kinematics for interactive applications

Vision-based human motion sensing has a strong merit that it does not impose any physical restrictions on humans, which provides a natural way of measuring human motion. However, its real-time processing is not easy to realize, because a human body has a high degrees of freedom, whose vision-based analysis is not simple and is usually time consuming. Here, we have developed a method in which human postures are analyzed from a limited number of visual cues. It is a combination of numerical analysis of inverse kinematics and visual search. Our method is based on a general framework of inverse kinematics, and, therefore, we can use relatively complex human figure model, which can generate natural human motion. In our experimental studies, we show that our implemented system works in real-time on a PC-cluster.