A study on smoothing for particle-filtered 3D human body tracking

Abstract

Stochastic models have become the dominant means of approaching the problem of articulated 3D human body tracking, where approximate inference is employed to tractably estimate the high-dimensional (~30D) posture space. Of these approximate inference techniques, particle filtering is the most commonly used approach. However filtering only takes into account past observations - almost no body tracking research employs smoothing to improve the filtered inference estimate, despite the fact that smoothing considers both past and future evidence and so should be more accurate. In an effort to objectively determine the worth of existing smoothing algorithms when applied to human body tracking, this paper investigates three approximate smoothed-inference techniques: particle-filtered backwards smoothing, variational approximation and Gibbs sampling. Results are quantitatively evaluated on both the HUMANEVA dataset as well as a scene containing occluding clutter. Surprisingly, it is found that existing smoothing techniques are unable to provide much improvement on the filtered estimate, and possible reasons as to why are explored and discussed.