Robust mean shift tracking with background information

Abstract

The background-weighted histogram (BWH) has been proposed in mean shift tracking algorithm to reduce the interference of background in target localization. However, the BWH also reduces the weight for part of complex object. Mean shift with BWH model is unable to track object with scale change. In this paper, we integrate an object/background likelihood model into the mean shift tracking algorithm. Experiments on both synthetic and real world video sequences demonstrate that the proposed method could effectively estimate the scale and orientation changes of the target. The proposed method can still robustly track the object when the target is not well initialized. © 2012 Springer-Verlag.