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dc.contributor.authorZhang, Baochangen_US
dc.contributor.authorGao, Yongshengen_US
dc.contributor.authorZhao, Sanqiangen_US
dc.contributor.authorZhong, Binengen_US
dc.contributor.editorEditor-in-Chief: Hamid Gharavien_US
dc.date.accessioned2017-05-03T15:01:11Z
dc.date.available2017-05-03T15:01:11Z
dc.date.issued2011en_US
dc.date.modified2011-10-18T07:26:16Z
dc.identifier.issn10518215en_US
dc.identifier.doi10.1109/TCSVT.2011.2105591en_AU
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.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent5677710 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherIEEE Circuits and Systems Societyen_US
dc.publisher.placeUnited Statesen_US
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofpagefrom29en_US
dc.relation.ispartofpageto38en_US
dc.relation.ispartofissue1en_US
dc.relation.ispartofjournalIEEE Transactions on Circuits and Systems for Video Technologyen_US
dc.relation.ispartofvolume21en_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchComputer Visionen_US
dc.subject.fieldofresearchPattern Recognition and Data Miningen_US
dc.subject.fieldofresearchcode080104en_US
dc.subject.fieldofresearchcode080109en_US
dc.titleKernel Similarity Modeling of Texture Pattern Flow for Motion Detection in Complex Backgrounden_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.rights.copyrightCopyright 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.en_AU
gro.date.issued2011
gro.hasfulltextFull Text


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