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Online multiple instance gradient feature selection for robust visual tracking
Xie, Yuan2; Qu, Yanyun1; Li, Cuihua1; Zhang, Wensheng2
Source PublicationPATTERN RECOGNITION LETTERS
2012-07-01
Volume33Issue:9Pages:1075-1082
SubtypeArticle
AbstractIn this paper, we focus on learning an adaptive appearance model robustly and effectively for object tracking. There are two important factors to affect object tracking, the one is how to represent the object using a discriminative appearance model, the other is how to update appearance model in an appropriate manner. In this paper, following the state-of-the-art tracking techniques which treat object tracking as a binary classification problem, we firstly employ a new gradient-based Histogram of Oriented Gradient (HOG) feature selection mechanism under Multiple Instance Learning (MIL) framework for constructing target appearance model, and then propose a novel optimization scheme to update such appearance model robustly. This is an unified framework that not only provides an efficient way of selecting the discriminative feature set which forms a powerful appearance model, but also updates appearance model in online MIL Boost manner which could achieve robust tracking overcoming the drifting problem. Experiments on several challenging video sequences demonstrate the effectiveness and robustness of our proposal. (C) 2012 Elsevier B.V. All rights reserved.
KeywordGradient-based Feature Selection Hog Multiple Instance Learning Online Object Tracking
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000304235500007
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/8007
Collection精密感知与控制研究中心_精密感知与控制
Affiliation1.Xiamen Univ, Dept Comp Sci, Video & Image Lab, Xiamen 361005, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Intelligent Control & Management Co, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Xie, Yuan,Qu, Yanyun,Li, Cuihua,et al. Online multiple instance gradient feature selection for robust visual tracking[J]. PATTERN RECOGNITION LETTERS,2012,33(9):1075-1082.
APA Xie, Yuan,Qu, Yanyun,Li, Cuihua,&Zhang, Wensheng.(2012).Online multiple instance gradient feature selection for robust visual tracking.PATTERN RECOGNITION LETTERS,33(9),1075-1082.
MLA Xie, Yuan,et al."Online multiple instance gradient feature selection for robust visual tracking".PATTERN RECOGNITION LETTERS 33.9(2012):1075-1082.
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