Para acceder a los documentos con el texto completo, por favor, siga el siguiente enlace:

Automatic learning of 3D pose variability in walking performances for gait analysis
Rius, Ignasi; González Sabaté, Jordi; Mozerov, Mikhail; Roca, Francesc Xavier
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.
Peer Reviewed
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
Attribution-NonCommercial-NoDerivs 3.0 Spain
Article - Draft

Mostrar el registro completo del ítem

Documentos relacionados

Otros documentos del mismo autor/a

Rius, Ignasi; Gonzàlez, Jordi; Mozerov, Mikhail; Roca, Francesc Xavier
Varona, Javier; González Sabaté, Jordi; Rius, Ignasi; Villanueva Pipaón, Juan José
Chakraborty, Bhaskar; Rius, Ignasi; Perdersoli, Marco; Mozerov, Mikhail; Gonzàlez, Jordi
Amato, Ariel; Huerta Casado, Iván; Mozerov, Mikhail; Roca Marva, Francesc Xavier; González Sabaté, Jordi
Mozerov, Mikhail; Amato, Ariel; Roca Marva, Francesc Xavier; González Sabaté, Jordi