Representing cyclic human motion using functional analysis
2005 (English)In: Image and Vision Computing, ISSN 0262-8856, Vol. 23, no 14, 1264-1276 p.Article in journal (Refereed) Published
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.
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
IdentifiersURN: urn:nbn:se:kth:diva-15282ISI: 000234243800003OAI: oai:DiVA.org:kth-15282DiVA: diva2:333323
QC 201005252010-08-052010-08-052011-12-01Bibliographically approved