背景加权直方图算法（BWH）在[2]中提出了尝试

减少干扰的背景均值漂移跟踪的目标定位。然而，

在本文中，我们证明了权重分配给候选目标区域的像素

BWH是那些没有背景资料成正比，即不会引入BWH

任何新的信息，因为均值漂移迭代公式是不变的规模

改造砝码。然后，我们提出了一个校正BWH（CBWH）的公式

只转型的目标模式，但不是目标候选模型。 CBWH计划

可以有效地降低背景的干扰，在目标定位。实验

结果表明，CBWH可能会导致更快的收敛速度和更准确的定位比

通常的目标表示均值漂移跟踪。即使目标没有得到很好的初始化，

该算法仍然强劲跟踪的对象，这是很难实现由

传统的目标表示。-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

conventiona