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Representing cyclic human motion using functional analysis




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.

Author(s): Ormoneit, D. and Black, M. J. and Hastie, T. and Kjellström, H.
Journal: Image and Vision Computing
Volume: 23
Number (issue): 14
Pages: 1264--1276
Year: 2005
Month: December

Department(s): Perceiving Systems
Bibtex Type: Article (article)
Paper Type: Journal

DOI: 10.1016/j.imavis.2005.09.004

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  title = {Representing cyclic human motion using functional analysis},
  author = {Ormoneit, D. and Black, M. J. and Hastie, T. and Kjellstr{\"o}m, H.},
  journal = {Image and Vision Computing},
  volume = {23},
  number = {14},
  pages = {1264--1276},
  month = dec,
  year = {2005},
  month_numeric = {12}