Visual tracking of multiple interacting objects through Rao-Blackwellized data association particle filtering

Abstract

A multiple object visual tracking framework is presented, which is able to manage complex object interactions, missing detections and clutter. The main contribution is the ability to deal with complex situations in which the interacting objects can change their dynamics while they are occluded. This is achieved by explicitly estimating putative locations of the occluded objects. The tracking is modeled by a Rao-Blackwellized Data Association Particle Filter (RBDAPF), which has a tractable substructure that allows to analytically compute the object positions, while the object-measurement associations are approximated by Particle Filtering. Besides improving the accuracy, this filter decomposition reduces the computational cost, since the complexity with the number of objects becomes linear instead of exponential. The Particle Filter efficiently manages the measurements from visible and occluded objects, the clutter, and missing measurements to estimate the correct data associations that lead to a robust tracking. Experimental results on surveillance videos show that the proposed RBDAPF framework is able to track multiple interacting objects in complex situations.