背景权重追踪

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.

块方向追踪

A scale and orientation adaptive mean shift tracking (SOAMST) algorithm is proposed in this paper to address the problem of how to estimate the scale and orientation changes of the target under the mean shift tracking framework. In the original mean shift tracking algorithm, the position of the target can be well estimated, while the scale and orientation changes can not be adaptively estimated. Considering that the weight image derived from the target model and the candidate model can represent the possibility that a pixel belongs to the target, we show that the original mean shift tracking algorithm can be derived using the zeroth and the first order moments of the weight image. With the zeroth order moment and the Bhattacharyya coefficient between the target model and candidate model, a simple and effective method is proposed to estimate the scale of target. Then an approach, which utilizes the estimated area and the second order center moment, is proposed to adaptively estimate the width, height and orientation changes of the target. Extensive experiments are performed to testify the proposed method and validate its robustness to the scale and orientation changes of the target.

块和方向追踪

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.

基于背景权重视频追踪

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.