基于非参数聚类和多尺度图像的目标跟踪

Target Tracking Based on Nonparametric Clustering and Multi-Scale Images

为了实现复杂背景下的目标空间定位和尺度定位,提出了一种基于非参数聚类和多尺度图像的目标跟踪算法.该算法首先利用改进的非参数颜色聚类方法自动划分目标颜色空间;然后使用高斯函数对颜色直方图中的每个颜色特征位的空域分布进行建模,并根据Bhattacharyya系数得到目标模型与候选模型的相似性函数;最后利用多尺度图像进行由粗到细的目标空间定位,同时利用推导的核函数自动带宽选择公式进行目标尺度定位.实验结果表明该算法优于典型的均值漂移跟踪方法.

In order to track a target in space and scale in a complex background, a target tracking algorithm based on the nonparametric clustering and multi-scale images is presented. In this algorithm, first, a modified nonparametric color-clustering method is employed to adaptively partition the color space of a tracked object, and the Gaussian function is used to model the spatial information of each bin of the color histogram. Next, the Bhattacharyya coefficient is adopted to derive a function describing the similarity between the target model and the target candidate. Then, a coarse-to-fine approach of multi-scale images is employed to implement the spatial location of the tracked object. Finally, the derived automatic bandwidth selection method of kernel function is applied to obtain the scale of the tracked object. Experimental results show that the proposed algorithm outperforms the classical mean shift tracker.