Robust mean-shift tracking with corrected background-weighted histogram

Abstract

Abstract:The background-weighted histogram (BWH) algorithm proposed in [2] attempts to reduce the interference of background in 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 a corrected BWH (CBWH) formula by transforming only the target model but not the target candidate model. The CBWH scheme can effectively reduce background's interference in target localization. The experimental results show that CBWH can lead to faster convergence and more accurate localization than the usual target representation in mean shift tracking. Even if the target is not well initialized, the proposed algorithm can still robustly track the object, which is hard to achieve by the conventional target representation.

A More Robust Mean Shift Tracker on Joint Color-CLTP Histogram

“…The idea of corrected background-weighted histogram and background model updating mechanism in [4] is adapted to reduce the interference of background that has color and texture features similar to the tracked target.…”

“…To evaluate the effectiveness of the proposed method, we carried out a series of experiments on five challenging public video sequences, Table Tennis playing video sequences from [4], Camera sequences from PETS2001 database [23], Bird2 video sequences from [24], Walking Woman video sequences from [25] and Panda video sequences from [26]. We make comparison with three state-of-the-art mean shift trackers, mean shift tracker with background-weighted histogram (MS_BWH) [3], mean shift tracker with corrected background-weighted histogram (MS_CBWH) [4] and mean shift tracker using joint color-LBP texture histogram (MS_RGBLBP) [5].…”

“…Among various object tracking methods, the mean shift tracker has recently attracted many researchers' attention [2] [3] [4] [5] [6] [7] [8] [9]due to its simplicity and efficiency.…”

“…Comaniciu et al [3] proposed a background-weighted histogram (BWH) to decrease background interference in target representation. However, Ning et al [4] demonstrated that the BWH-based mean shift tracker is equivalent to the conventional mean shift tracking method [2], and then a corrected background-weighted histogram (CBWH) is proposed to actually reduce the interference of background in target localization. Additionally, only utilizing color histogram to model the target object in the mean shift algorithm has the disadvantage that the spatial information of the target object is lost.…”

“…The CLTP is firstly applied to represent the target texture features, and then combined with color to form a distinctive and effective target representation called joint color-CLTP histogram. In order to reduce the interference of background in target localization, corrected backgroundweighted histogram and background update mechanism [4] is adapted to decrease the weights of both prominent background color and texture features similar to the target. The proposed tracker is more robust especially in case of heavy occlusions and complex background changes than several state-of-art variants of mean shift tracker.…”