多滤波器支持相关滤波跟踪算法
Multi-Filter Support Correlation Filters Tracking Algorithm
苏振扬 1程云 1黄克斌 1宋国柱 1万俊2
作者信息
- 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
- 折叠
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%.
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引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据