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Incremental Tensor Subspace Learning and Its Applications to Foreground Segmentation and Tracking
Hu, Weiming1; Li, Xi1; Zhang, Xiaoqin2; Shi, Xinchu1; Maybank, Stephen3; Zhang, Zhongfei4
Source PublicationINTERNATIONAL JOURNAL OF COMPUTER VISION
2011-02-01
Volume91Issue:3Pages:303-327
SubtypeArticle
AbstractAppearance modeling is very important for background modeling and object tracking. Subspace learning-based algorithms have been used to model the appearances of objects or scenes. Current vector subspace-based algorithms cannot effectively represent spatial correlations between pixel values. Current tensor subspace-based algorithms construct an offline representation of image ensembles, and current online tensor subspace learning algorithms cannot be applied to background modeling and object tracking. In this paper, we propose an online tensor subspace learning algorithm which models appearance changes by incrementally learning a tensor subspace representation through adaptively updating the sample mean and an eigenbasis for each unfolding matrix of the tensor. The proposed incremental tensor subspace learning algorithm is applied to foreground segmentation and object tracking for grayscale and color image sequences. The new background models capture the intrinsic spatiotemporal characteristics of scenes. The new tracking algorithm captures the appearance characteristics of an object during tracking and uses a particle filter to estimate the optimal object state. Experimental evaluations against state-of-the-art algorithms demonstrate the promise and effectiveness of the proposed incremental tensor subspace learning algorithm, and its applications to foreground segmentation and object tracking.
KeywordIncremental Learning Tensor Subspace Foreground Segmentation Tracking
WOS HeadingsScience & Technology ; Technology
WOS KeywordROBUST VISUAL TRACKING ; SHADOW SEGMENTATION ; APPEARANCE MODELS ; REPRESENTATION ; SURVEILLANCE ; RECOGNITION ; PEOPLE ; OBJECT ; CUES
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000286610300005
Citation statistics
Cited Times:81[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/3248
Collection模式识别国家重点实验室_视频内容安全
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Wenzhou Univ, Coll Math & Informat Sci, Wenzhou 325000, Zhejiang, Peoples R China
3.Univ London Birkbeck Coll, Dept Comp Sci & Informat Syst, London WC1E 7HX, England
4.SUNY Binghamton, Binghamton, NY 13902 USA
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Hu, Weiming,Li, Xi,Zhang, Xiaoqin,et al. Incremental Tensor Subspace Learning and Its Applications to Foreground Segmentation and Tracking[J]. INTERNATIONAL JOURNAL OF COMPUTER VISION,2011,91(3):303-327.
APA Hu, Weiming,Li, Xi,Zhang, Xiaoqin,Shi, Xinchu,Maybank, Stephen,&Zhang, Zhongfei.(2011).Incremental Tensor Subspace Learning and Its Applications to Foreground Segmentation and Tracking.INTERNATIONAL JOURNAL OF COMPUTER VISION,91(3),303-327.
MLA Hu, Weiming,et al."Incremental Tensor Subspace Learning and Its Applications to Foreground Segmentation and Tracking".INTERNATIONAL JOURNAL OF COMPUTER VISION 91.3(2011):303-327.
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