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dc.contributor.authorZhang, Baochang
dc.contributor.authorGao, Yongsheng
dc.contributor.authorZhao, Sanqiang
dc.contributor.authorZhong, Bineng
dc.contributor.editorEditor-in-Chief: Hamid Gharavi
dc.date.accessioned2017-05-03T15:01:11Z
dc.date.available2017-05-03T15:01:11Z
dc.date.issued2011
dc.date.modified2011-10-18T07:26:16Z
dc.identifier.issn1051-8215
dc.identifier.doi10.1109/TCSVT.2011.2105591
dc.identifier.urihttp://hdl.handle.net/10072/40128
dc.description.abstractThis paper proposes a novel kernel similarity modeling of texture pattern flow (KSM-TPF) for background modeling and motion detection in complex and dynamic environments. The texture pattern flow encodes the binary pattern changes in both spatial and temporal neighborhoods. The integral histogram of texture pattern flow is employed to extract the discriminative features from the input videos. Different from existing uniform threshold based motion detection approaches which are only effective for simple background, the kernel similarity modeling is proposed to produce an adaptive threshold for complex background. The adaptive threshold is computed from the mean and variance of an extended Gaussian mixture model. The proposed KSM-TPF approach incorporates machine learning method with feature extraction method in a homogenous way. Experimental results on the publicly available video sequences demonstrate that the proposed approach provides an effective and efficient way for background modeling and motion detection.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent5677710 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherIEEE Circuits and Systems Society
dc.publisher.placeUnited States
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom29
dc.relation.ispartofpageto38
dc.relation.ispartofissue1
dc.relation.ispartofjournalIEEE Transactions on Circuits and Systems for Video Technology
dc.relation.ispartofvolume21
dc.rights.retentionY
dc.subject.fieldofresearchComputer vision
dc.subject.fieldofresearchcode460304
dc.titleKernel Similarity Modeling of Texture Pattern Flow for Motion Detection in Complex Background
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.rights.copyright© 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
gro.date.issued2011
gro.hasfulltextFull Text
gro.griffith.authorGao, Yongsheng


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