Dynamic Proposal Variance and Optimal Particle Allocation in Particle Filtering for Video Tracking

This paper presents a novel particle allocation approach to particle filtering which minimizes the total tracking distortion for a fixed number of particles over a video sequence. We define the tracking distortion as the variance of the error between the true state and estimated state and use rate-distortion theory to determine the optimal particle number and memory size allocation under fixed particle number and memory constraints, respectively. We subsequently provide an algorithm for simultaneous adjustment of the proposal variance and particle number for optimal particle allocation in video tracking systems. Experimental results are used to evaluate the proposed video tracking system and demonstrate its utility for target tracking in numerical examples and video sequences. We demonstrate the superiority of the proposed dynamic proposal variance and optimal particle allocation algorithm in comparison to traditional particle allocation methods, i.e., a fixed number of particles per frame.