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Learning an Intrinsic-Variable Preserving Manifold for Dynamic Visual Tracking
Qiao, Hong1; Zhang, Peng2,3; Zhang, Bo3; Zheng, Suiwu
2010-06-01
Source PublicationIEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
ISSN1083-4419
Volume40Issue:3Pages:868-880
AbstractManifold learning is a hot topic in the field of computer science, particularly since nonlinear dimensionality reduction based on manifold learning was proposed in Science in 2000. The work has achieved great success. The main purpose of current manifold-learning approaches is to search for independent intrinsic variables underlying high dimensional inputs which lie on a low dimensional manifold. In this paper, a new manifold is built up in the training step of the process, on which the input training samples are set to be close to each other if the values of their intrinsic variables are close to each other. Then, the process of dimensionality reduction is transformed into a procedure of preserving the continuity of the intrinsic variables. By utilizing the new manifold, the dynamic tracking of a human who can move and rotate freely is achieved. From the theoretical point of view, it is the first approach to transfer the manifold-learning framework to dynamic tracking. From the application point of view, a new and low dimensional feature for visual tracking is obtained and successfully applied to the real-time tracking of a free-moving object from a dynamic vision system. Experimental results from a dynamic tracking system which is mounted on a dynamic robot validate the effectiveness of the new algorithm.
KeywordFeature extraction robotic visual tracking visual tracking
DOI10.1109/TSMCB.2009.2031559
Language英语
Funding ProjectChinese Academy of Sciences ; National Natural Science Foundation (NNSF) of China[60675039] ; National Natural Science Foundation (NNSF) of China[60621001] ; 863 Program of China[2006AA04Z217] ; 863 Program of China[2007AA04Z228] ; NNSF of China[60725310] ; NNSF of China[90820007] ; 973 Program of China[2007CB311002]
WOS Research AreaAutomation & Control Systems ; Computer Science
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000277774700028
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/11063
Collection中国科学院数学与系统科学研究院
Corresponding AuthorQiao, Hong
Affiliation1.Chinese Acad Sci, Lab Complex Syst & Intelligent Sci, Inst Automat, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Grad Sch, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Inst Appl Math, Acad Math & Syst Sci, Beijing 100190, Peoples R China
4.Chinese Acad Sci, State Key Lab Sci & Engn Comp, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Qiao, Hong,Zhang, Peng,Zhang, Bo,et al. Learning an Intrinsic-Variable Preserving Manifold for Dynamic Visual Tracking[J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS,2010,40(3):868-880.
APA Qiao, Hong,Zhang, Peng,Zhang, Bo,&Zheng, Suiwu.(2010).Learning an Intrinsic-Variable Preserving Manifold for Dynamic Visual Tracking.IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS,40(3),868-880.
MLA Qiao, Hong,et al."Learning an Intrinsic-Variable Preserving Manifold for Dynamic Visual Tracking".IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 40.3(2010):868-880.
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