Classification of Human Body Motion

The classification of human body motion is a difficult problem. In particular, the automatic segmentation of 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 here that the importance sampling can be done in linear time, in place 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. Performance is promising, but there remains a residual misclassification rate and possible explanations for this are discussed.