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Discriminative feature selection for visual tracking
Ma JK(马俊凯); Luo HB(罗海波); Zhou, Wei; Song YC(宋颖超); Hui B(惠斌); Chang Z(常铮)
Department光电信息技术研究室
Conference Name6th Conference on Advances in Optoelectronics and Micro/Nano-Optics, AOM 2017
Conference DateApril 23-26, 2017
Conference PlaceNanjing, China
Author of SourceJiangsu Optical Society; Southeast University; The Optical Society of America
Source Publication6th Conference on Advances in Optoelectronics and Micro/Nano-Optics
PublisherIOP
Publication PlaceBristol, UK
2017
Pages1-8
Indexed ByEI ; CPCI(ISTP)
EI Accession number20172803930014
WOS IDWOS:000412799900046
Contribution Rank1
ISSN1742-6588
AbstractVisual tracking is an important role in computer vision tasks. The robustness of tracking algorithm is a challenge. Especially in complex scenarios such as clutter background, illumination variation and appearance changes etc. As an important component in tracking algorithm, the appropriateness of feature is closed related to the tracking precision. In this paper, an online discriminative feature selection is proposed to provide the tracker the most discriminative feature. Firstly, a feature pool which contains different information of the image such as gradient, gray value and edge is built. And when every frame is processed during tracking, all of these features will be extracted. Secondly, these features are ranked depend on their discrimination between target and background and the highest scored feature is chosen to represent the candidate image patch. Then, after obtaining the tracking result, the target model will be update to adapt the appearance variation. The experiment show that our method is robust when compared with other state-of-the-art algorithms. © Published under licence by IOP Publishing Ltd.
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/20796
Collection光电信息技术研究室
Corresponding AuthorMa JK(马俊凯)
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
2.University of Chinese Academy of Sciences, Beijing, China
3.Key Laboratory of Opto-Electronic Information Processing CAS, Shenyang, China
4.AVIC Jiangxi HONGDU Aviation Industry Group LTD, Nanchang, China
Recommended Citation
GB/T 7714
Ma JK,Luo HB,Zhou, Wei,et al. Discriminative feature selection for visual tracking[C]//Jiangsu Optical Society; Southeast University; The Optical Society of America. Bristol, UK:IOP,2017:1-8.
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