针对传统目标跟踪算法中存在的特征鲁棒性不强的问题,提出了一种基于背景加权空间直方图的目标跟踪算法.算法通过综合背景加权直方图和空间直方图二者各自的优势,增强了特征描述的鲁棒性,进而在Mean Shift跟踪框架下实现了对运动目标的跟踪.仿真实验与定量分析表明,所提算法能够实现对目标的稳定跟踪,跟踪所得的平均中心位置误差较传统算法降低了80%以上.

In order to improve the feature robustness of the traditional object tracking algorithm,an improved algorithm based on background-weighted spatial histogram was proposed.It combines the background-weighted histogram and spatial histogram and exploits the advantages of both features,which distinctively improves the feature robustness.The proposed algorithm was implemented under the framework of Mean Shift tracking and obtained a stable tracking.Experimental results indicate that the proposed algorithm can well track the object and obtains at least 80% decreasing of the mean center location error than that of the traditional algorithms.