一个比例和方向自适应均值漂移跟踪算法（SOAMST）

提出本文所要解决的问题，如何估计的规模和方向

改变均值漂移下的目标跟踪框架。在原来的均值偏移

跟踪算法，可以很好地估计目标的位置，规模的同时，

方向的变化，不能自适应估计。考虑到图像（重量）

是来自于目标运动模型和候选模型可以代表的可能性，一个

像素属于目标，我们证明了原来的均值漂移跟踪算法可以

推导出的重量图像的零阶和一阶矩。随着零阶

矩和目标模型和候选模型之间的Bhattacharyya系数，

提出了简单而有效的方法来估计的规模为目标。然后一种方法，

利用估计的区域和第二阶中心矩，提出

自适应地估计目标的宽度，高度和方向的变化。广泛

实验来证实所提出的方法，并验证其可靠性

规模和方向变化的目标。-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 e