一种分层Mean Shift目标跟踪算法

针对经典Mean shift (MS)目标跟踪算法的颜色特征鲁棒差、匹配迭代复杂的缺点, 提出一种分层Mean shift (Hierarchical mean shift, HMS)目标跟踪算法. 首先通过MS迭代将目标区域特征空间的数据点聚类于模式点, 使得以简洁的方式描述前景跟踪目标, 建立目标模型与目标候选模型的聚类模式点描述, 进行聚类块匹配. 然后, 导出聚类块模式点匹配下的相似度量函数, 进行像素点匹配, 结合邻域一致性, 计算像素平移量, 分层估计序列帧中跟踪目标质心模式点的位置, 并给出HMS匹配迭代跟踪算法. 实验结果表明, 与其他两种MS跟踪算法相比, HMS既能提高序列帧跟踪目标表达与匹配的鲁棒性, 又无需匹配所有数据点, 算法简洁且有效可行.

A Hierarchical Mean Shift Algorithm for Object Tracking

We propose a hierarchical mean shift (HMS) algorithm for object tracking. Firstly, cluster modal points are obtained by mean-shift iteratively processing all the data points in the region so that they can represent foreground object in a succinct manner. The target model and the target candidate model are described by the cluster modal points, and match processes of clustered blocks are performed. Then, on the basis of cluster blocks match, similarity measure function is set up to match between target model and target candidate at pixel level. And the pixel shift vector of target is calculated with the introduction of the neighborhood consistency concept. So, the centroid of tracking object is got layer by layer in the consecutive frames, and the HMS match iteration for object tracking is presented. Experimental comparisons with other two MS algorithms demonstrate the validity and performance of the proposed algorithm.