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Improved Kernel Correlation Filter Tracking with Gaussian scale space
Tan SK(谭舒昆); Liu YP(刘云鹏); Li YC(李义翠)
作者部门光电信息技术研究室
会议名称International Symposium on Infrared Technology and Application and the International Symposiums on Robot Sensing and Advanced Control
会议日期May 9-11, 2016
会议地点Beijing
会议录名称Proceedings of SPIE - The International Society for Optical Engineering
出版者SPIE
出版地Bellingham, WA
2016
页码1-7
收录类别EI ; CPCI(ISTP)
EI收录号20170503310011
WOS记录号WOS:000391228600103
产权排序1
ISSN号0277-786X
ISBN号978-1-5106-0772-9
关键词Visual Object Tracking Kernel Correlation Filter Gaussian Scale Space
摘要

Recently, Kernel Correlation Filter (KCF) has achieved great attention in visual tracking filed, which provide excellent tracking performance and high possessing speed. However, how to handle the scale variation is still an open problem. In this paper, focusing on this issue that a method based on Gaussian scale space is proposed. Firstly, we will use KCF to estimate 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 it to estimate target image under different scales. Combine with the scale parameter of scale space, for each corresponding 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, use Mean Absolute Difference (MAD) as the match criterion. After getting the optimal matching in the image with the template, we will get the best zoom ratio s, consequently estimate the target size. In the experiments, compare with CSK, KCF etc. demonstrate that the proposed method achieves high improvement in accuracy, is an efficient algorithm.

语种英语
引用统计
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/19160
专题光电信息技术研究室
通讯作者Tan SK(谭舒昆)
作者单位1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China
2.University of Chinese Academy of Sciences, Beijing, 100049, China
3.Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Science, Shenyang, 110016, China
4.Key Lab of Image Understanding and Computer Vision, Liaoning Province, Shenyang, 110016, China
推荐引用方式
GB/T 7714
Tan SK,Liu YP,Li YC. Improved Kernel Correlation Filter Tracking with Gaussian scale space[C]. Bellingham, WA:SPIE,2016:1-7.
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