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dc.contributor.authorÖzer, Sedat
dc.contributor.authorCirpan, Hakan Ali
dc.contributor.authorKabaoǧlu, Nihat
dc.date.accessioned2019-06-28T11:11:40Z
dc.date.available2019-06-28T11:11:40Z
dc.date.issued2006
dc.identifier.isbn3540393927
dc.identifier.isbn9783540393924
dc.identifier.issn3029743
dc.identifier.urihttps://hdl.handle.net/20.500.12469/1663
dc.description.abstractThis paper addresses the problem of applying powerful statistical pattern classification algorithm based on kernel functions to target tracking on surveillance systems. Rather than directly adapting a recognizer we develop a localizer directly using the regression form of the Support Vector Machines (SVM). The proposed approach considers to use dynamic model together as feature vectors and makes the hyperplane and the support vectors follow the changes in these features. The performance of the tracker is demonstrated in a sensor network scenario with a constant velocity moving target on a plane for surveillance purpose. © Springer-Verlag Berlin Heidelberg 2006.
dc.language.isoEnglish
dc.publisherSpringer Verlag
dc.titleSupport vector regression for surveillance purposes
dc.typeConference Paper
dc.identifier.startpage442
dc.identifier.endpage449
dc.relation.journalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.identifier.volume4105 LNCS
dc.contributor.khasauthorKabaoǧlu, Nihat


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