Adaptive resource allocation in particle filtering for articulated object tracking

This paper presents a novel particle allocation approach to particle filtering for articulated object tracking which minimizes the total tracking distortion given a fixed number of particles over a video sequence. Under the framework of decentralized articulated object tracking, we propose the dynamic proposal variance and optimal particle number allocation algorithm for articulated object tracking to allocate particles among different parts of the articulated object as well as different frames. 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 part in each frame. To the best of our knowledge, our approach is the first to provide an optimal allocation of a fixed number of particles among different object parts and different frames.