A particle filter for tracking densely populated objects based on explicit multiview occlusion analysis

A novel particle filter is presented for tracking densely populated objects moving on a two-dimensional plane; it is based on a probabilistic framework of explicit multiview occlusion analysis. The spatial structure of 2-D occlusion process between objects is modeled as a hidden process controlled by a Markov probability structure. The tracking problem is then formulated as a recursive Bayesian framework for solving the simultaneous estimation problem of two interactive processes; hypothesis generation/testing of the occlusion structure and the computation of posterior probability distribution of object states such as position and pose. For efficient implementation of the formulated framework, we develop a novel particle filter in which each particle can support multiple posterior distributions of object states on different occlusion hypotheses. Experiments using synthetic and real data confirm the robustness of the proposed method even in the face of severe occlusion.