Optimized particles for 3-D tracking

Abstract

3-D visual tracking is useful for many of its applications. In this paper, we propose two different ways for different system configurations to optimize particle filter for enhancing 3-D tracking performances. On one hand, a new data fusion method is proposed to obtain the optimal importance density function for active vision systems. On the other hand, we develop a method for reconfigurable vision systems to maximize the effective sampling size in particle filter, which consequentially helps to solve the degeneracy problem and minimize the tracking error. © 2010 Springer-Verlag.