Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/7091
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dc.contributor.authorBarış, İpek-
dc.contributor.authorBaştanlar, Yalın-
dc.date.accessioned2019-02-05T13:44:56Z
dc.date.available2019-02-05T13:44:56Z
dc.date.issued2018-03
dc.identifier.citationBarış, İ., and Baştanlar, Y. (2018, October 16-19). Classification and tracking of traffic scene objects with hybrid camera systems. Paper presented at the 20th IEEE International Conference on Intelligent Transportation Systems, ITSC 2017. doi:10.1109/ITSC.2017.8317588en_US
dc.identifier.isbn978-153861525-6
dc.identifier.urihttps://doi.org/10.1109/ITSC.2017.8317588
dc.identifier.urihttp://hdl.handle.net/11147/7091
dc.description20th IEEE International Conference on Intelligent Transportation Systems, ITSC 2017; Mielparque YokohamaYokohama, Kanagawa; Japan; 16 October 2017 through 19 October 2017en_US
dc.description.abstractIn a hybrid camera system combining an omnidirectional and a Pan-Tilt-Zoom (PTZ) camera, the omnidirectional camera provides 360 degree horizontal field-of-view, whereas the PTZ camera provides high resolution at a certain direction. This results in a wide field-of-view and high resolution camera system. In this paper, we exploit this hybrid system for real-time object classification and tracking for traffic scenes. The omnidirectional camera detects the moving objects and performs an initial classification using shape-based features. Concurrently, the PTZ camera classifies the objects using high resolution frames and Histogram of Oriented Gradients (HOG) features. PTZ camera also performs high-resolution tracking for the objects classified as the target class by the omnidirectional camera. The object types we worked on are pedestrian, motorcycle, car and van. Extensive experiments were conducted to compare the classification accuracy of the hybrid system with single camera alternatives.en_US
dc.description.sponsorshipTUBITAK Project No: 113E107en_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.ispartof20th IEEE International Conference on Intelligent Transportation Systems, ITSC 2017en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectHybrid camera systemen_US
dc.subjectObject detectionen_US
dc.subjectOmnidirectional cameraen_US
dc.subjectVehicle detectionen_US
dc.titleClassification and tracking of traffic scene objects with hybrid camera systemsen_US
dc.typeConference Objecten_US
dc.authoridTR176747en_US
dc.institutionauthorBarış, İpek-
dc.institutionauthorBaştanlar, Yalın-
dc.departmentİzmir Institute of Technology. Computer Engineeringen_US
dc.identifier.volume2018en_US
dc.identifier.startpage1en_US
dc.identifier.endpage6en_US
dc.identifier.wosWOS:000432373000007en_US
dc.identifier.scopus2-s2.0-85046288603en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1109/ITSC.2017.8317588-
dc.relation.doi10.1109/ITSC.2017.8317588en_US
dc.coverage.doi10.1109/ITSC.2017.8317588en_US
item.languageiso639-1en-
item.fulltextWith Fulltext-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextopen-
item.openairetypeConference Object-
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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