Bayesian tracking implemented as a particle filter is one of the most used techniques for full-body human tracking. However, given the high-dimensionality of the models to be tracked, the number of required particles to properly populate the space of solutions make the problem computationally very expensive. To overcome this, we present an efficient scheme which makes use of an action model that guides the prediction step of the particle filter. In this manner, particles are propagated to locations in the search space with most a posteriori information. Hence, we sample from a smooth motion model only those postures which are feasible given a particular action. We show, that this scheme improves the efficiency and accuracy of the overall tracking approach.