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Automatic learning of 3D pose variability in walking performances for gait analysis
dc.contributor.author | Rius, Ignasi |
dc.contributor.author | González Sabaté, Jordi |
dc.contributor.author | Mozerov, Mikhail |
dc.contributor.author | Roca, Francesc Xavier |
dc.date.accessioned | 2011-11-06T18:49:02Z |
dc.date.available | 2011-11-06T18:49:02Z |
dc.date.issued | 2008 |
dc.identifier.citation | Rius, I. [et al.]. Automatic learning of 3D pose variability in walking performances for gait analysis. "International Journal for Computational Vision and Biomechanics", 2008, vol. 1, núm. 1, p. 33-43. |
dc.identifier.issn | 0973-6778 |
dc.identifier.uri | http://hdl.handle.net/2117/13744 |
dc.description.abstract | This paper proposes an action specific model which automatically learns the variability of 3D human postures observed in a set of training sequences. First, a Dynamic Programing synchronization algorithm is presented in order to establish a mapping between postures from different walking cycles, so the whole training set can be synchronized to a common time pattern. Then, the model is trained using the public CMU motion capture dataset for the walking action, and a mean walking performance is automatically learnt. Additionally statistics about the observed variability of the postures and motion direction are also computed at each time step. As a result, in this work we have extended a similar action model successfully used for tracking, by providing facilities for gait analysis and gait recognition applications. |
dc.format.extent | 11 p. |
dc.language.iso | eng |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo |
dc.subject.lcsh | Computer vision |
dc.subject.lcsh | Human motion modelling |
dc.subject.lcsh | Gair analysis and recognition |
dc.subject.lcsh | Dynamic programming |
dc.title | Automatic learning of 3D pose variability in walking performances for gait analysis |
dc.type | Article |
dc.subject.lemac | Visió per ordinador |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | http://paginas.fe.up.pt/~ijcvb/editions_v1_n1.htm |
dc.rights.access | Open Access |
local.identifier.drac | 2249624 |
dc.description.version | Preprint |
local.citation.publicationName | International Journal for Computational Vision and Biomechanics |
local.citation.volume | 1 |
local.citation.number | 1 |
local.citation.startingPage | 33 |
local.citation.endingPage | 43 |