Simultaneous particle tracking in multi-action motion models with synthesized paths

Abstract

This paper proposes human motion models of multiple actions for 3D pose tracking. A training pose sequence of each action, such as walking and jogging, is separately recorded by a motion capture system and modeled independently. This independent modeling of action-specific motions allows us 1) to optimize each model in accordance with only its respective motion and 2) to improve the scalability of the models. Unlike existing approaches with similar motion models (e.g. switching dynamical models), our pose tracking method uses the multiple models simultaneously for coping with ambiguous motions. For robust tracking with the multiple models, particle filtering is employed so that particles are distributed simultaneously in the models. Efficient use of the particles can be achieved by locating many particles in the model corresponding to an action that is currently observed. For transferring the particles among the models in quick response to changes in the action, transition paths are synthesized between the different models in order to virtually prepare inter-action motions. Experimental results demonstrate that the proposed models improve accuracy in pose tracking.