动态特征和静态特征自适应融合的目标跟踪算法  

Research on visual object tracking by fusing dynamic and static features

作　　者：张立朝[1] 毕笃彦[1] 查宇飞[1] 汪云飞[1] 马时平[1] 

机构地区：[1]空军工程大学航空航天工程学院,陕西西安710038

出　　处：《西安电子科技大学学报》2015年第6期164-172,共9页Journal of Xidian University

基　　金：国家自然科学基金资助项目(61472442,61203268,61202339)

摘　　要：由于大多数目标跟踪算法只采用单一静态特征或单一动态特征对目标建模,但静态特征模型不能描述目标的动态特性,并且很难适应场景复杂、快速移动和旋转等问题;而传统运动光流能够描述局部动态特性,却存在孔径问题.提出一种自适应融合动态特征和静态特征的跟踪方法:通过双向光流预测和误差度量自适应提取动态特征,并提取候选目标区域的静态特征,然后构造融合权重函数有效地融合动态特征和静态特征并以此构造协方差矩阵估计误差椭圆,准确描述目标尺度和方向,实现对目标精确表示;通过on-line参数更新机制对权重分配参数进行更新,实现动态特征和静态特征分配的自适应调节,能够适应目标运动速度的变化和场景变化.实验结果表明,在背景复杂的情况下,当目标快速移动或旋转时,与其他相关算法相比,该算法能够获得更好的跟踪效果.Traditionally, most tracking algorithms only use the single static feature or single dynamic feature to model the object. The static feature based model can not describe the object＇s dynamic characteristics and is difficult to adapt to the changing object with a background cluster, abrupt movement and rotations. While the classical optical flow is able to describe local dynamic characteristics, it has aperture issues. Therefore, we present a new tracking method based on fusing the dynamic and static features adaptively： the dynamic feature is extracted by the bidirectional optical flow and error metric adaptively, and is fused with the static feature by the fusion weight efficiently. The fusion weight based covariance is constructed to evaluate error ellipse which describes the object＇s scale and orientation exactly; the weight assignment parameter is updated by an on-line parameter updating mechanism, which balances the dynamic feature and static feature and ensures the tracking adaptation to the object＇s velocity and scene changes. Experiments show that the proposed algorithm can achieve better tracking results compared with the related algorithms, on the occasions when the object moves abruptly and rotates with a background cluster.

关 键 词：动态特征 运动度量 静态特征 特征融合 自适应 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]