Visual tracking via weakly supervised learning from multiple imperfect oracles

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dc.contributor.authorZhong, B.
dc.contributor.authorYao, H.
dc.contributor.authorChen, S.
dc.contributor.authorJi, R.
dc.contributor.authorChin, T.
dc.contributor.authorWang, H.
dc.date.issued2014
dc.description.abstractAbstract not available
dc.description.statementofresponsibilityB. Zhong, H. Yao, S. Chen, R. Ji, T.J. Chin, H. Wang
dc.identifier.citationPattern Recognition, 2014; 47(3):1395-1410
dc.identifier.doi10.1016/j.patcog.2013.10.002
dc.identifier.issn0031-3203
dc.identifier.issn1873-5142
dc.identifier.urihttp://hdl.handle.net/2440/102219
dc.language.isoen
dc.publisherElsevier
dc.rightsCrown Copyright © 2013 Published by Elsevier Ltd. All rights reserved
dc.source.urihttp://www.sciencedirect.com/science/article/pii/S0031320313004081
dc.subjectVisual tracking; Weaklysupervisedlearning; Information fusion; Online learning; Adaptiveappearancemodel; Drift problem; Online evaluation
dc.titleVisual tracking via weakly supervised learning from multiple imperfect oracles
dc.typeJournal article
pubs.publication-statusPublished

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