Adaptive Particle-Distortion Tradeoff Control in Particle Filtering for Video Tracking

This paper presents a novel approach to particle filtering which minimizes the total tracking distortion by considering dynamic variance of proposal density and optimal number of particles for each frame. Traditionally, particle filters use fixed variance of proposal density and fixed number of particles per frame. We propose a tracking distortion measurement and use rate distortion theory to obtain the optimal memory size and particle number allocation equations under different two constraints. We subsequently propose the dynamic proposal variance and optimal particle number allocation algorithm for video tracking systems. Simulation results show the improved performance of our proposed algorithm in comparison to traditional particle filters. To the best of our knowledge, our approach is the first to consider variant numbers of particles for each frame.