Posture Constraints for Bayesian Human Motion Tracking

Abstract

[eng] One of the most used techniques for full-body human tracking consists of estimating the probability of the parameters of a human body model over time by means of a particle filter. However, given the high-dimensionality of the models to be tracked, the number of required particles to properly populate the space of solutions makes the problem computationally very expensive. To overcome this, we present an efficient scheme which makes use of an action-specific model of human postures to guide the prediction step of the particle filter, so only feasible human postures are considered. As a result, the prediction step of this model-based tracking approach samples from a first order motion model only those postures which are accepted by our action-specific model. In this manner, particles are propagated to locations in the search space with most a posteriori information avoiding particle wastage. We show that this scheme improves the efficiency and accuracy of the overall tracking approach.