Optimal Particle Allocation in Particle Filtering for Multiple Object Tracking

Abstract

In our previous work, we proposed an approach to particle filtering which simultaneously adjusts the proposal variance and number of particles for each frame, in order to minimize the tracking distortion for single object tracking. In this paper, we extend our previous work to multiple object video tracking. Under the framework of distributed multiple object tracking, we propose the tracking distortion and use rate distortion theory to derive the optimal particle allocation among multiple targets as well as multiple frames. We subsequently propose a dynamic proposal variance and optimal particle number allocation algorithm for multi-object tracking. Experimental results show the superior performance of our proposed algorithm to traditional particle allocation methods, i.e., a fixed number of particles for each object in each frame. The proposed algorithm can also be used in decentralized articulated object tracking. To the best of our knowledge, this paper is the first to provide an optimal allocation of a fixed number of particles among multiple objects and frames.