计算机工程与应用2017，Vol.53Issue(1)：29-33,141,6.DOI:10.3778/j.issn.1002-8331.1605-0430
基于高斯尺度空间的核相关滤波目标跟踪算法
Improved kernel correlation filter tracking with Gaussian scale space
摘要
Abstract
Recently, Kernel Correlation Filter(KCF)has achieved great attention in visual tracking field, which provides excellent computation performance and high possessing speed. However, how to handle the scale variation is still an open problem. Focusing on this issue, a method based on Gaussian scale space is proposed. Firstly, this paper uses KCF to esti-mate the location of the target, the context region which includes the target and its surrounding background will be the image to be matched. In order to get the matching image of a Gaussian scale space, image with Gaussian kernel convolution can be got. After getting the Gaussian scale space of the image to be matched, then, according to the Gaussian scale space image, it estimates target image under different scales. It combines with the scale parameter of scale space, for each corre-sponding scale image performing bilinear interpolation operation to change the size to simulate target imaging at different scales. Finally, matching the template with different size of images with different scales, the paper uses Mean Absolute Difference(MAD)as the match criterion. After getting the optimal matching in the image, it ascertains the best zoom ra-tios, consequently estimates the target size. In the experiments, compare with CSK, KCF, the results demonstrate that the proposed method achieves high improvement in accuracy and is an efficient algorithm.关键词
目标跟踪/核相关滤波/高斯尺度空间/双线性插值/平均绝对误差Key words
visual tracking/Kernel Correlation Filter(KCF)/Gaussian scale space/bilinear interpolation/Mean Absolute Difference(MAD)分类
信息技术与安全科学引用本文复制引用
谭舒昆,刘云鹏,李义翠..基于高斯尺度空间的核相关滤波目标跟踪算法[J].计算机工程与应用,2017,53(1):29-33,141,6.基金项目
中国科学院国防科技创新重点基金（No.CXJJ-14-Z65）。 （）
