目标窗口尺寸自适应变化的Mean-Shift跟踪算法  

Mean-Shift tracking algorithm with adaptive bandwidth of target

作　　者：林庆[1,2] 陈远祥[2] 王士同[1,3] 詹永照[2] 

机构地区：[1]南京理工大学计算机系,南京210094 [2]江苏大学计算机科学与通信工程学院,江苏镇江212013 [3]江南大学信息学院,江苏无锡214122

出　　处：《计算机应用》2009年第12期3329-3331,3335,共4页journal of Computer Applications

基　　金：国家自然科学基金资助项目(60673190)

摘　　要：传统的窗宽尺寸固定不变的Mean-Shift跟踪算法不能实时地适应目标尺寸大小的变化。将多尺度空间理论与Kalman滤波器相结合,利用Kalman滤波器对尺寸变化的目标面积比例进行预测,用多尺度空间理论中的目标信息量度量方法求出前后相邻两帧的目标特征信息比,将其作为Kalman滤波器的观察值对目标面积比例进行修正,然后与Mean-Shift算法结合起来对目标进行跟踪。实验结果表明,改进的跟踪算法对尺度逐渐变大和变小的目标都能连续自动地选择合适大小的跟踪窗口。The traditional Mean-Shift tracking algorithm of the fixed window-size cannot be adapted to real-time goal of the changes in size. Multi-scale space theory was combined with Kalman filter. First, Kalman filter was introduced to predict the proportion of the target image area, and then this proportion was revised by the observation, which was the proportion of the information of the two adjacent target images using the measurement of the target amount of information in the muhscale space theory. Finally, it was implemented by the combination of the Mean-Shift tracking algorithm and Kalman filter to track targets. The improved algorithm can select the proper size of the tracking window in the scenarios that not only of increasing scale but of decreasing scale by the experimental results.

关 键 词：KALMAN滤波器 信息量度量 Mean—Shift算法 面积的变化比例 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]