Mean Shift Tracking with Advanced Background-Weighted Histogram

Abstract

Tracking objects in videos using mean shift technique has brought to public attention. In background-weighted histogram (BWH) algorithm proposed by Kernel-Based Object tracking attempts to reduce the interference of background target localization in mean shift tracking. However, in this paper we prove that the weights assigned to pixels in the target candidate region by BWH are proportional to those without background information, i.e. BWH does not introduce any new information because the mean shift iteration formula is invariant to the scale transformation of weights. We then propose an Advanced BWH (ABWH) formula by transforming only the target model but not the target candidate model. The ABWH scheme can effectively reduce background’s interference in target localization and for the target not well initialized, it can still robustly track the object, which is hard to achieve by the conventional target representation and it lead to faster convergence and more accurate localization than the usual target representation in mean shift tracking.