Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10359636 | Image and Vision Computing | 2005 | 13 Pages |
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.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Dirk Ormoneit, Michael J. Black, Trevor Hastie, Hedvig Kjellström,