Comparison of support vector machines and deep learning for vehicle detection

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dc.contributor.authorKaplan Ö.
dc.contributor.authorŞaykol E.
dc.date.accessioned2024-03-13T10:00:56Z
dc.date.available2024-03-13T10:00:56Z
dc.date.issued2018
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description3rd International Conference on Recent Trends and Applications in Computer Science and Information Technology, RTA-CSIT 2018 -- 23 November 2018 through 24 November 2018 -- -- 143396en_US
dc.description.abstractThe main goal of this paper is to compare different Vehicle Detection algorithms and to provide an effective comparison technique for developers and researchers. During this study, fine tunings are suggested to improve the implementations of these algorithms. Our focus on Support Vector Machines (SVM) and then Deep Learning based approaches. The SVM based vehicle detection implementation utilizes Histogram Oriented Gradients (HOG). The deep learning approach we consider is the YOLO implementation. Our evaluation employs 400 random frames extracted from a real world driving video. As stated by the experimental results, YOLO is more accurate with %81.9 success than SVM which only scored %57.8. © 2018 CEUR-WS.en_US
dc.identifier.endpage69en_US
dc.identifier.issn1613-0073
dc.identifier.scopus2-s2.0-85059866165en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage64en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12662/2872
dc.identifier.volume2280en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherCEUR-WSen_US
dc.relation.ispartofCEUR Workshop Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.titleComparison of support vector machines and deep learning for vehicle detectionen_US
dc.typeConference Objecten_US

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