SIA OpenIR  > 光电信息技术研究室
Improved Kernel Correlation Filter Tracking with Gaussian scale space
Tan SK(谭舒昆); Liu YP(刘云鹏); Li YC(李义翠)
Department光电信息技术研究室
Conference NameInternational Symposium on Infrared Technology and Application and the International Symposiums on Robot Sensing and Advanced Control
Conference DateMay 9-11, 2016
Conference PlaceBeijing
Source PublicationProceedings of SPIE - The International Society for Optical Engineering
PublisherSPIE
Publication PlaceBellingham, WA
2016
Pages1-7
Indexed ByEI ; CPCI(ISTP)
EI Accession number20170503310011
WOS IDWOS:000391228600103
Contribution Rank1
ISSN0277-786X
ISBN978-1-5106-0772-9
KeywordVisual Object Tracking Kernel Correlation Filter Gaussian Scale Space
Abstract

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.

Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/19160
Collection光电信息技术研究室
Corresponding AuthorTan SK(谭舒昆)
Affiliation1.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
Recommended Citation
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.
Files in This Item: Download All
File Name/Size DocType Version Access License
Improved kernel corr(784KB)会议论文 开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Tan SK(谭舒昆)]'s Articles
[Liu YP(刘云鹏)]'s Articles
[Li YC(李义翠)]'s Articles
Baidu academic
Similar articles in Baidu academic
[Tan SK(谭舒昆)]'s Articles
[Liu YP(刘云鹏)]'s Articles
[Li YC(李义翠)]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Tan SK(谭舒昆)]'s Articles
[Liu YP(刘云鹏)]'s Articles
[Li YC(李义翠)]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Improved kernel correlation filter tracking with Gaussian scale space .pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.