Swapping-based Annealed Particle Filter with Occlusion Handling for 3D Human Body Tracking

Abstract

In this paper, we propose a new approach for 3D human body tracking. We first extend the idea of Swapping-based Partitioned Sampling (SBPS), which was introduced by Dubuisson et al. for solving the articulated object tracking problem in high dimensional state spaces. This extension aims to deal with self-occlusion and constraints between parts of the human body, which are not taken into account in SBPS. We prove that, under the same assumptions required by SBPS, the posterior distribution are correctly estimated in our framework. We then introduce a new approach for 3D human body tracking, based on this new framework and Annealed Particle Filter (APF). Experiments with multi-camera walking sequences from the HumanEva I dataset show the efficiency of the proposed approach in terms of both accuracy and computation time.