改进的核相关滤波跟踪算法  

Improved kernelized correlation filter tracking

作　　者：孙健[1,2] 向伟 谭舒昆[1,2] 刘云鹏 SUN Jian;XIANG Wei;TAN Shukun;LIU Yunpeng(Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110016,China;University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国科学院沈阳自动化研究所,沈阳110016 [2]中国科学院大学,北京100049

出　　处：《计算机工程与应用》2018年第9期178-182,共5页Computer Engineering and Applications

基　　金：中国科学院国防科技创新重点基金(No.CXJJ-14-Z65)

摘　　要：主要针对核相关滤波(KCF)跟踪算法无法解决目标跟踪中尺度变化及目标丢失问题,提出了一种改进的核相关滤波目标跟踪算法。在训练位移滤波器的基础上增加了一个尺度滤波器来改进目标尺度变化问题。为解决目标丢失问题,结合了遮挡处理机制,当判断目标受到遮挡面积较小时使用支持向量机(SVM)对样本进行在线训练,当目标遮挡时使用再检测分类器进行检测。实验结果表明,该方法与其他优秀跟踪算法比较跟踪精度有明显提升。An improved kernel correlation filtering target tracking algorithm is proposed by Kernelized Correlation Filtering(KCF)tracking algorithm,which can not solve the problem of scale change and out-of-view in target tracking.Firstly,a scale filter is added to improve the target scale change based on training translation filter.In order to solve out-of-view problem,the occlusion processing mechanism is used.When the target is not completely occluded,the SVM is used to train the samples,and the re-detection classifier is adopted to detect.Experimental results show that the tracking accuracy of this method is obviously improved in comparison with other excellent tracking algorithms.

关 键 词：核相关滤波 目标丢失 尺度变化 遮挡 支持向量机 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]