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Representing cyclic human motion using functional analysis
FOI, Stockholm.ORCID iD: 0000-0002-5750-9655
2005 (English)In: Image and Vision Computing, ISSN 0262-8856, Vol. 23, no 14, 1264-1276 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2005. Vol. 23, no 14, 1264-1276 p.
Keyword [en]
human motion, functional data analysis, missing data, singular value decomposition, principal component analysis, motion capture, tracking, temporal templates, image sequences, gait analysis, recognition, tracking, capture, camera, models, video
URN: urn:nbn:se:kth:diva-15282ISI: 000234243800003OAI: diva2:333323
QC 20100525Available from: 2010-08-05 Created: 2010-08-05 Last updated: 2011-12-01Bibliographically approved

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Kjellström, Hedvig
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