Target tracking method based on the fusion of structured SVM and KCF algorithm
作者单位：Guangdong Institute of Intelligent ManufacturingGuangdong Key Laboratory of Modern Control Technology
会议名称：《第32届中国控制与决策会议》
会议日期：2020年
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 080902[工学-电路与系统] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 081104[工学-模式识别与智能系统] 080203[工学-机械设计及理论] 0802[工学-机械工程] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
关 键 词：target tracking KCF structured SVM scale change drift
摘 要：Aiming at the drift problem of traditional KCF(kernelized correlation filter) target tracking algorithm caused by scale change and fast movement,a tracking method based on the fusion of structured SVM(support vector machine)and KCF algorithm is *** with the location and scale information of the tracking target,a structured SVM classification and prediction model is established to replace the ridge regression filtering method in KCF,train the samples collected by cyclic matrix,and solve the problem of not detailed *** addition,a search strategy optimization method based on tracking target motion features is proposed to reduce unnecessary search time in dense sampling,further improve the search efficiency and classifier accuracy of traditional KCF algorithm in dense sampling,improve its calculation efficiency and target tracking performance in complex *** can solve the problem that the target cannot be accurately tracked due to the drift caused by the change of target scale and fast *** show that the algorithm can locate the target area more accurately when the target scale changes and fast *** with the traditional KCF tracking algorithm,it has better performance and stronger robustness.