Temporal-Spatial Local Gaussian Process Experts with Vision Based Human Motion Tracking
Satyanarayana Mummana, T. Ravi Kiran · 2012
Human pose estimation via motion tracking systems can be considered as a regression problem within a discriminative framework. It is always a challenging task to model the mapping from observation space to state space because of the high dimensional characteristic in the multimodal conditional distribution. In order to build the mapping, existing techniques usually involve large set of training samples in the learning process which are limited in their capability to deal with multimodality. We propose , in this work, a novel online sparse Gaussian process regression model to recover 3-D human motion in monocular videos. Particularly, we investigate the fact that for a given test input, its output is mainly determined by the training samples potentially residing in its local neighborhood and defined in the unified input-output space.