PT Journal
AU Mikhail Mozerov
   Ignasi Rius
   Xavier Roca
   Jordi Gonzalez
TI Nonlinear synchronization for automatic learning of 3D pose variability in human motion sequences
SO EURASIP Journal on Advances in Signal Processing
JI EURASIPJ
PY 2010
DI 10.1155/2010/507247
AB Article ID 507247A dense matching algorithm that solves the problem of synchronizing prerecorded human motion sequences, which show different speeds and accelerations, is proposed. The approach is based on minimization of MRF energy and solves the problem by using Dynamic Programming. Additionally, an optimal sequence is automatically selected from the input dataset to be a time-scale pattern for all other sequences. The paper utilizes an action specific model which automatically learns the variability of 3D human postures observed in a set of training sequences. 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. The synchronized motion sequences are used to learn a model of human motion for action recognition and full-body tracking purposes.
ER