Smart particle filtering for high-dimensional tracking

Abstract

Tracking articulated structures like a hand or body within a reasonable time is challenging because of the high-dimensionality of the state space. Recently, a new optimization method, called ‘ Stochastic Meta-Descent’ (SMD) has been introduced in computer vision. This is a gradient descent scheme with adaptive and parameter-specific step sizes able to operate in a constrained space. However, while the local optimization works very well, reaching the global optimum is not guaranteed. We therefore propose an enhanced algorithm that wraps a particle filter around multiple SMD-based trackers, which play the role of many particles, i.e. that act as ‘smart particles’. After the standard particle propagation on the basis of a simple motion model, SMD is performed and the resulting new particle set is included such that the original Bayesian distribution is not altered. The resulting ‘ Smart Particle Filter’ (SPF) tracks high-dimensional articulated structures with far fewer samples than previous methods. Additionally, it can handle multiple hypotheses and clutter, where pure optimization approaches have problems. Good performance is demonstrated for the case of hand tracking from 3D range data.