利用团块模型进行目标跟踪

Appearance model based on blobs for object tracking

提出了一种基于局部特征的目标跟踪算法.通过多尺度分析方法,根据颜色和空间上的相似性将目标分割为多个区域,每个区域由一个团块表示,团块包含了该区域所有像素的颜色均值、形状和位置.根据团块特征构造目标的外观模型,定义团块的匹配准则,通过团块匹配进行目标跟踪.由于目标模型是基于局部特征的,并且包含目标的全局空间结构,因此该算法在局部遮挡和目标尺度变化的情况下,依然能够进行准确地跟踪.实验表明该算法能够有效实现复杂场景下的目标跟踪,性能优于Mean-shift算法.

This paper proposes a novel local feature-based object tracking algorithm. The object is segmented into a few regions using multiple scale segmentation, and each region is denoted as a blob which combines the local color, shape and location. An object appearance model is built based on the blob feature. The object is tracked by the blob matching criterion defined. The appearance model includes the local color and spatial structures, so the object can be tracked in the condition of partial occlusion and change of the object scale Examples demonstrate that the proposed algorithm can obtain the accurate tracking results in the complex environment and has better performance than the well-known mean shift algorithm.