Robust mean shift tracking with improved Background-weighted histogram

Tracking objects in videos using mean shift technique has brought to public attention. In this paper, we developed an improved tracking algorithm based on the mean shift framework. To represent the object model more accurately, the motion direction of the object which was estimated by the local motion filters was employed to weight the histogram. Besides, a wise object template updating strategy was proposed to adapt to the change of the object appearance caused by noise, deformation or occlusion. The experimental results on several real world scenarios shows that our approach has an excellent tracking performance comparing with the background weighted histogram mean shift tracking approach and traditional mean shift tracking method.