Combining Foreground / Background Feature Points and Anisotropic Mean Shift For Enhanced Visual Object Tracking

This paper proposes a novel visual object tracking scheme, exploiting both local point feature correspondences and global object appearance using the anisotropic mean shift tracker. Using a RANSAC cost function incorporating the mean shift motion estimate, motion smoothness and complexity terms, an optimal feature point set for motion estimation is found even when a high proportion of outliers is presented. The tracker dynamically maintains sets of both foreground and background features, the latter providing information on object occlusions. The mean shift motion estimate is further used to guide the inclusion of new point features in the object model. Our experiments on videos containing long term partial occlusions, object intersections and cluttered or close color distributed background have shown more stable and robust tracking performance in comparison to three existing methods.