Automatic learning of 3D pose variability in walking performances for gait analysis
Ignasi Rius, Jordi Gonzàlez, M. G. Mozerov, F. Xavier Roca · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2008
This paper proposes an action specic 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 dierent walking cycles, so the whole training set can be synchronised 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.