Multiview Occlusion Analysis for Tracking Densely Populated Objects Based on 2-D Visual Angles

Abstract

A novel framework of multiview occlusion analysis is presented for tracking densely populated objects moving on two-dimensional plane. This paper explicitly models the spatial structure of the occlusion process between objects and its uncertainty, based on 2D silhouette-based visual angles from fixed viewpoints. The occlusion structure is defined as tangency combination between the objects and the edges of the visual angles, based on geometric constraints inherent in the visual angles. The problem is then formulated as recursive Bayesian estimation consisting of hypothesis generation/testing of the occlusion structure and the estimation of posterior probability distribution for the object states including position and posture, on each hypothesis of the occlusion structure. For implementing the proposed framework, we develop a novel type of particle filter that supports multiple state distributions. Experiments using synthetic and real data show the robustness of the framework even in the face of severe occlusions.