软件导刊2024，Vol.23Issue(12)：220-225,6.DOI:10.11907/rjdk.241637
多滤波器支持相关滤波跟踪算法
Multi-Filter Support Correlation Filters Tracking Algorithm
摘要
Abstract
In the support correlation filter tracking method,calculations are converted into frequency domain by cyclic sampling,which elimi-nates 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 bet-ter 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Key words
multi-filter/SVM/support correlation filter/visual tracking分类
信息技术与安全科学引用本文复制引用
苏振扬,程云,黄克斌,宋国柱,万俊..多滤波器支持相关滤波跟踪算法[J].软件导刊,2024,23(12):220-225,6.基金项目
国家自然科学基金面上项目(71974073) （71974073）
黄冈市教育科学规划项目(2023JB05) （2023JB05）
黄冈师范学院博士基金项目(2042024165) （2042024165）
