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Representing cyclic human motion using functional analysis
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DOI：10.1016/j.imavis.2005.09.004.png)
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Abstract
En 中文
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. (C) 2005 Elsevier B.V. All rights reserved.
Keywords:
human motion
functional data analysis
missing data
singular value decomposition
principal component analysis
motion capture
tracking
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