Learning and Tracking Cyclic Human Motion

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 principal components of the cycles are computed using a new algorithm that accounts for missing information and enforces smooth transitions 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.

• Type: Conference
• Year: 2000
• Where: NIPS
• Authors: Dirk Ormoneit, Hedvig Sidenbladh, Michael J. Black, Trevor Hastie
• Keywords: Human Motions, Monocular Video Sequences, NIPS 2000, NIPS 2007, Periodic Human Motion