基于Mean-shift的改进目标跟踪算法

传统的Mean-shift目标跟踪算法对背景因素比较敏感，采用核加权直方图的方法计算目标模板与候选区域目标特征往往无法实现对运动目标的准确定位。在研究传统算法的基础上，改进了Mean-shift算法中目标特征选取机制，即目标模板采用背景加权，候选目标区域采用核加权。仿真结果表明，该方法实现了在复杂环境背景下对运动目标更加准确的跟踪。

Improved target tracking algorithm based on Mean-shift

Traditional Mean-shift target tracking algorithm is rather sensitive to background environment. The use of kernel weighted histogram for target template and target candidates can not always get exact center of the target. Therefore, an improved feature selection mechanism was proposed, in which background weighted histogram was chosen for target template and kernel weighted histogram for target candidates. The simulation results show that the method achieves more accurate target tracking in complex environments.