Robust Multi-Camera 3D People Tracking with Partial Occlusion Handling

Abstract

This paper presents an approach to robust 3D people tracking using multiple synchronized and calibrated cameras. The goal is to improve people tracking accuracy when the subjects being tracked partially occlude each other in some of the camera views. To achieve this goal, Monte Carlo fine-tuning is deployed to rectify 3D people locations obtained from partially occluded image observations. In our approach, Gaussian mixture models and axis-parallel ellipsoids are used to represent the appearance and the 3D body structures of the subjects, respectively. Related parameters are learned off-line. Experimental results obtained using real videos illustrate that the proposed approach is capable of accurate and robust 3D people tracking under partial or complete occlusions.