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2000

Learning and Tracking Cyclic Human Motion

9 years 11 months ago
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.
Dirk Ormoneit, Hedvig Sidenbladh, Michael J. Black
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 2000
Where NIPS
Authors Dirk Ormoneit, Hedvig Sidenbladh, Michael J. Black, Trevor Hastie
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