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dc.contributor.authorÖzer, Sedat
dc.contributor.authorÇırpan, Hakan Ali
dc.contributor.authorKabaoǧlu, Nihat
dc.date.accessioned2019-06-27T08:06:52Z
dc.date.available2019-06-27T08:06:52Z
dc.date.issued2006
dc.identifier.isbn978-1-4244-0238-0
dc.identifier.urihttps://hdl.handle.net/20.500.12469/1239
dc.identifier.urihttps://ieeexplore.ieee.org/document/1659718
dc.description.abstractThis 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.
dc.language.isoTurkish
dc.publisherIEEE
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.titleSupport vector machines based target tracking techniques
dc.typeProceedings Paper
dc.identifier.startpage369
dc.identifier.endpage+
dc.relation.journal2006 IEEE 14th Signal Processing And Communications Applications, Vols 1 and 2
dc.identifier.wosWOS:000245347800094
dc.contributor.khasauthorÖzer, Sedat
dc.contributor.khasauthorÇırpan, Hakan Ali
dc.contributor.khasauthorKabaoǧlu, Nihat


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