Robust object tracking using particle filters and multi-region mean shift

Abstract

In this paper, we introduce a novel algorithm which builds upon the combined anisotropic mean-shift and particle filter framework. The anisotropic mean-shift [4] with 5 degrees of freedom, is extended to work on a partition of the object into concentric rings. This adds spatial information to the description of the object which makes the algorithm more resilient to occlusion and less likely to mistake the object with other objects having similar color densities. Experiments conducted on videos containing deformable objects with long-term partial occlusion (or, short-term full occlusion) and intersection have shown robust tracking performance, especially in tracking objects with long term partial occlusion, short term full occlusion, close color background clutter, severe object deformation and fast changing motion. Comparisons with two existing methods have shown marked improvement in terms of robustness to occlusions, tightness and accuracy of tracked box, and tracking drifts. © 2009 Springer-Verlag Berlin Heidelberg.