Representing cyclic human motion using functional analysis

Authors:
Dirk Ormoneit;Michael J. Black;Trevor Hastie;Hedvig Kjellström
Affiliations:
Marshall Wace LLP, 1/11 John Adam Street, London WC2N 6HT, UK;Department of Computer Science, Brown University, 115 Waterman Street, Box 1910, Providence, RI 02912, USA;Department of Statistics, Stanford University, Stanford, CA 94305, USA;Department of IR Systems, Swedish Defence Research Agency, SE-164 90 Stockholm, Sweden
Venue:
Image and Vision Computing
Year:
2005

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Abstract

We present a robust automatic method for modeling cyclic 3D human motion such as walking using motion-capture data. The pose of the body is represented by a time-series of joint angles which are automatically segmented into a sequence of motion cycles. The mean and the principal components of these cycles are computed using a new algorithm that enforces smooth transitions between the cycles by operating in the Fourier domain. Key to this method is its ability to automatically deal with noise and missing data. A learned walking model is then exploited for Bayesian tracking of 3D human motion.