一种核相关滤波器的多尺度目标跟踪方法  

A Multi-Scale Target Tracking Method Based on Kernelized Correlation Filter

作　　者：李远状 韩彦芳[1] 于书盼 LI Yuanzhuang, HAN Yanfang, YU Shupan(School of Optical - Electrical and Computer Engineering, University of Shanghai tor Science and Technology, Shanghai 200093, China)

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《电子科技》2018年第10期1-5,10,共6页Electronic Science and Technology

基　　金：国家自然科学基金(61672354)

摘　　要：针对传统的核相关滤波器(KCF)跟踪算法无法解决目标尺度变化并导致目标丢失的问题,文中提出了一种尺度自适应核相关滤波分类器。首先利用核岭回归方法对由循环移位得到的大量目标图像样本进行训练得到核相关滤波分类器;然后建立多尺度待检测图像集,通过相关滤波器求取最大响应以得到当前目标位置与尺度信息;最后利用新目标图像为训练样本在线更新目标的尺度和外观信息。为了验证算法的有效性,在数据集中选取10组测试序列进行验证,并同时与KCF、DSST、CN等优秀算法进行对比。实验结果表明,所提算法能更好的适应尺度变化的跟踪,且跟踪精度有所提升。Aiming at the problem that traditional Kernelized CoiTelation Filter （KCF） tracking algorithm failedto solve the target scale change and target loss, a scale adaptive kernel correlation filter classifier was proposed in thestudy. Firstly, a large number of target image samples obtained by cyclic shift were trained by using kernel ridge re-gression to obtain a kernel - dependent filter classifier. Then, the multi - scale image set to he detected was estab-lished, and the cunent target position and scale inibmlation were obtained by the COiTelation filter. Finally, the newtarget images were used to update the target＇s scale and appearance intbmlation online. In order to verity the validityof the algorithm, 10 sets of test sequences were selected for verification in datasets. Besides, its peromlance wascompared with other competitive trackers such as KCF、DSST、CN. Experimental results showed that the proposed al-gorithm could better applied in tracking with scale variation and improved the tracking accuracy.

关 键 词：目标跟踪 核相关滤波 核岭回归 循环移位 尺度池 自适应尺度 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]