Learning and Tracking Cyclic Human Motion

Part of Advances in Neural Information Processing Systems 13 (NIPS 2000)

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Dirk Ormoneit, Hedvig Sidenbladh, Michael Black, Trevor Hastie


We present methods for learning and tracking human motion in video. We estimate a statistical model of typical activities from a large set of 3D periodic human motion data by segmenting these data automatically into "cycles". Then the mean and the princi(cid:173) pal components of the cycles are computed using a new algorithm that accounts for missing information and enforces smooth tran(cid:173) sitions between cycles. The learned temporal model provides a prior probability distribution over human motions that can be used in a Bayesian framework for tracking human subjects in complex monocular video sequences and recovering their 3D motion.