Classification of human body motion

Abstract

The classification of human body motion is a difficult problem. In particular, the automatic segmentation of image sequences containing more than one class of motion is challenging. An effective approach is to use mixed discrete/continuous states to couple perception with classification. A spline contour is used to track the outline of the person. We show that, for a quasi-periodic human body motion, an autoregressive process is a suitable model for the contour dynamics. This can then be used as a dynamical model for mixed-state "condensation" filtering, switching automatically between different motion classes. We have developed "partial importance sampling" to enhance the efficiency of the mixed-state condensation filter. It is also shown that the importance sampling can be done in linear time, instead of the previous quadratic algorithm. "Tying" of discrete states is used to obtain further efficiency improvements. Automatic segmentation is demonstrated on video sequences of aerobic exercises. The performance is promising, but there remains a residual misclassification rate, and possible explanations for this are discussed.