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题名:
Improved Kernel Correlation Filter Tracking with Gaussian scale space
作者: 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
通讯作者: 谭舒昆
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: Proceedings of SPIE - The International Society for Optical Engineering
会议录出版者: SPIE
会议录出版地: Bellingham, WA
出版日期: 2016
页码: 1-7
收录类别: EI ; CPCI(ISTP)
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
产权排序: 1
EI收录号: 20170503310011
WOS记录号: WOS:000391228600103
Citation statistics:
内容类型: 会议论文
URI标识: http://ir.sia.cn/handle/173321/19160
Appears in Collections:光电信息技术研究室_会议论文

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Recommended Citation:
Tan SK,Liu YP,Li YC. Improved Kernel Correlation Filter Tracking with Gaussian scale space[C]. International Symposium on Infrared Technology and Application and the International Symposiums on Robot Sensing and Advanced Control. Beijing. May 9-11, 2016.Improved Kernel Correlation Filter Tracking with Gaussian scale space.
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