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Representing cyclic human motion using functional analysis
FOI, Stockholm.ORCID-id: 0000-0002-5750-9655
2005 (engelsk)Inngår i: Image and Vision Computing, ISSN 0262-8856, E-ISSN 1872-8138, Vol. 23, nr 14, s. 1264-1276Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
2005. Vol. 23, nr 14, s. 1264-1276
Emneord [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
Identifikatorer
URN: urn:nbn:se:kth:diva-15282ISI: 000234243800003OAI: oai:DiVA.org:kth-15282DiVA, id: diva2:333323
Merknad
QC 20100525Tilgjengelig fra: 2010-08-05 Laget: 2010-08-05 Sist oppdatert: 2017-12-12bibliografisk kontrollert

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