多滤波器支持相关滤波跟踪算法  

Multi-Filter Support Correlation Filters Tracking Algorithm

作　　者：苏振扬 程云 黄克斌 宋国柱 万俊 SU Zhenyang;CHENG Yun;HUANG Kebin;SONG Guozhu;WAN Jun(School of Education,Huanggang Normal University,Huanggang 438000,China;School of Information and Safety Engineering,Zhongnan University of Economics and Law,Wuhan 430073,China)

机构地区：[1]黄冈师范学院教育学院,湖北黄冈438000 [2]中南财经政法大学信息与安全学院,湖北武汉430073

出　　处：《软件导刊》2024年第12期220-225,共6页Software Guide

基　　金：国家自然科学基金面上项目(71974073);黄冈市教育科学规划项目(2023JB05);黄冈师范学院博士基金项目(2042024165)。

摘　　要：支持相关滤波跟踪方法利用循环采样将计算转换到频域进行,解决了支持向量机采样少与计算量大的问题。但是当前跟踪方法在模型更新时将历史样本和当前样本进行线性插值,不能很好地利用样本的历史信息。针对该问题,提出多滤波器支持相关滤波跟踪方法。在跟踪过程中,首先利用历史样本训练历史滤波器,其次利用历史滤波器对当前滤波器进行约束,从而能够更好地利用样本的历史信息。在OTB100数据库上的实验表明,该算法精确率达到79.2%,成功率达到58.6%。相较于尺度核化支持相关滤波算法(SKSCF),该算法在精确率与成功率上分别提高了2%和3.7%。In the support correlation filter tracking method,calculations are converted into frequency domain by cyclic sampling,which eliminates less sampling and high computational complexity of support vector machine(SVM).However,the current method linearly interpolates historical and current samples during the tracking process to obtain training samples,which cannot effectively utilize the historical information of the samples.In response to this issue,this paper proposes a multi filter supported correlation filtering tracking method.During the tracking process,first train the historical filter using historical samples,and then use the historical filter to constrain the current filter,which can better utilize the historical information of the samples.Experiments on the OTB100 database showed that the algorithm achieved an accuracy of 79.2%and a success rate of 58.6%.Compared to the Scale Kernel Support Correlation Filtering algorithm(SKSCF),the algorithm proposed in this paper improved accuracy and success rate respectively by 2%and 3.7%.

关 键 词：多滤波器 支持向量机 支持相关滤波 目标跟踪 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]