Support Vector Machines Based Target Tracking Techniques

dc.contributor.author Özer, Sedat
dc.contributor.author Çırpan, Hakan Ali
dc.contributor.author Kabaoğlu, Nihat
dc.date.accessioned 2019-06-27T08:06:52Z
dc.date.available 2019-06-27T08:06:52Z
dc.date.issued 2006
dc.department Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü en_US
dc.department Yüksekokullar, Teknik Bilimler Meslek Yüksekokulu en_US
dc.description.abstract This paper addresses the problem of aplying powerful statistical pattern classification algorithms based on kernels to target tracking. 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 using 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 demostrated in a sensor network scenario with a moving target in a polynomial route. en_US]
dc.identifier.citationcount 0
dc.identifier.endpage +
dc.identifier.isbn 978-1-4244-0238-0
dc.identifier.startpage 369 en_US
dc.identifier.uri https://hdl.handle.net/20.500.12469/1239
dc.identifier.wos WOS:000245347800094 en_US
dc.institutionauthor Özer, Sedat en_US
dc.institutionauthor Çırpan, Hakan Ali en_US
dc.institutionauthor Kabaoğlu, Nihat en_US
dc.language.iso tr en_US
dc.publisher IEEE en_US
dc.relation.journal 2006 IEEE 14th Signal Processing And Communications Applications, Vols 1 and 2 en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.title Support Vector Machines Based Target Tracking Techniques en_US
dc.type Conference Object en_US
dc.wos.citedbyCount 1
dspace.entity.type Publication

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