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Automatic learning of 3D pose variability in walking performances for gait analysis

AutorRius, Ignasi; Gonzàlez, Jordi; Mozerov, Mikhail; Roca, F. Xavier
Palabras claveComputer vision
Human motion modelling
Gair analysis and recognition
Dynamic programming
Fecha de publicaciónene-2008
EditorSerials Publications
CitaciónInternational Journal for Computational Vision and Biomechanics 1(1): 33-43 (2008)
ResumenThis 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.
Descripción11 pages, 5 figures, 3 tables.
Versión del editorhttp://paginas.fe.up.pt/~ijcvb/editions_v1_n1.htm
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