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Título:	Automatic learning of 3D pose variability in walking performances for gait analysis
Autor/a:	Rius, Ignasi; González Sabaté, Jordi; Mozerov, Mikhail; Roca, Francesc Xavier
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.
Abstract:	Peer Reviewed
Materia(s):	-Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo -Computer vision -Human motion modelling -Gair analysis and recognition -Dynamic programming -Visió per ordinador
Derechos:	Attribution-NonCommercial-NoDerivs 3.0 Spain http://creativecommons.org/licenses/by-nc-nd/3.0/es/
Tipo de documento:	Artículo - Borrador Artículo
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