Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5492
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dc.contributor.authorKaraimer, Hakkı Can-
dc.contributor.authorÇınaroğlu, İbrahim-
dc.contributor.authorBaştanlar, Yalın-
dc.date.accessioned2017-05-12T12:00:45Z-
dc.date.available2017-05-12T12:00:45Z-
dc.date.issued2015-10-
dc.identifier.citationKaraimer, H. C., Çınaroğlu, İ., and Baştanlar, Y. (2015, September). Combining shape-based and gradient-based classifiers for vehicle classification. Paper presented at the 18th IEEE International Conference on Intelligent Transportation Systems. doi:10.1109/ITSC.2015.135en_US
dc.identifier.issn2153-0009-
dc.identifier.urihttp://doi.org/10.1109/ITSC.2015.135-
dc.identifier.urihttp://hdl.handle.net/11147/5492-
dc.description18th IEEE International Conference on Intelligent Transportation Systems, ITSC 2015; Palacio de Congresos de Canarias Avenida Principe de Asturias Gran Canaria; Spain; 15 September 2015 through 18 September 2015en_US
dc.description.abstractIn this paper, we present our work on vehicle classification with omnidirectional cameras. In particular, we investigate whether the combined use of shape-based and gradient-based classifiers outperforms the individual classifiers or not. For shape-based classification, we extract features from the silhouettes in the omnidirectional video frames, which are obtained after background subtraction. Classification is performed with kNN (k Nearest Neighbors) method, which has been commonly used in shape-based vehicle classification studies in the past. For gradient-based classification, we employ HOG (Histogram of Oriented Gradients) features. Instead of searching a whole video frame, we extract the features in the region located by the foreground silhouette. We use SVM (Support Vector Machines) as the classifier since HOG+SVM is a commonly used pair in visual object detection. The vehicle types that we worked on are motorcycle, car and van (minibus). In experiments, we first analyze the performances of shape-based and HOG-based classifiers separately. Then, we analyze the performance of the combined classifier where the two classifiers are fused at decision level. Results show that the combined classifier is superior to the individual classifiers. © 2015 IEEE.en_US
dc.description.sponsorshipTUBITAK (project 113E107)en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relationinfo:eu-repo/grantAgreement/TUBITAK/EEEAG/113E107en_US
dc.relation.ispartof18th IEEE International Conference on Intelligent Transportation Systemsen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCombined classifieren_US
dc.subjectHistogram of oriented gradientsen_US
dc.subjectOmnidirectional camerasen_US
dc.subjectVehicle classificationen_US
dc.subjectGradient baseden_US
dc.subjectShape baseden_US
dc.titleCombining shape-based and gradient-based classifiers for vehicle classificationen_US
dc.typeConference Objecten_US
dc.authoridTR176747en_US
dc.institutionauthorKaraimer, Hakkı Can-
dc.institutionauthorÇınaroğlu, İbrahim-
dc.institutionauthorBaştanlar, Yalın-
dc.departmentİzmir Institute of Technology. Computer Engineeringen_US
dc.identifier.volume2015en_US
dc.identifier.startpage800en_US
dc.identifier.endpage805en_US
dc.identifier.wosWOS:000376668800128en_US
dc.identifier.scopus2-s2.0-84950266060en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1109/ITSC.2015.135-
dc.relation.doi10.1109/ITSC.2015.135en_US
dc.coverage.doi10.1109/ITSC.2015.135en_US
dc.identifier.wosqualityttpTop10%en_US
item.openairetypeConference Object-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextopen-
crisitem.author.dept03.04. Department of Computer Engineering-
Appears in Collections:Computer Engineering / Bilgisayar Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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