Real-time human motion analysis for human-machine interface

This paper presents real-time human motion analysis for human-machine interface. In general, man-machine 'smart' interface requires real-time human motion capturing systems without special devices or markers. Although vision-based human motion capturing systems do not use such special devices and markers, they are essentially unstable and can only acquire partial information because of self-occlusion. When we analyze full-body motion, the problem becomes more severer. Therefore, we have to introduce a robust pose estimation strategy to deal with relatively poor results of image analysis. To solve this problem, we have developed a method to estimate full-body human postures, where an initial estimation is acquired by real-time inverse kinematics and, based on the estimation, more accurate estimation is searched for referring to the processed image. The key point is that our system can estimate full-body human postures from limited perceptual cues such as positions of a head, hands and feet, which can be stably acquired by silhouette contour analysis.