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dc.contributor.authorKaraimer, Hakki Can
dc.contributor.authorBastanlar, Yaln
dc.date.accessioned2021-02-12T18:47:46Z
dc.date.available2021-02-12T18:47:46Z
dc.date.issued2015
dc.identifier.isbn978-1-4673-7386-9
dc.identifier.issn2165-0608
dc.identifier.urihttps://hdl.handle.net/11147/10307
dc.description23nd Signal Processing and Communications Applications Conference (SIU)en_US
dc.descriptionBastanlar, Yalin/0000-0002-3774-6872en_US
dc.description.abstractWe describe a shape-based method for classification of vehicles from omnidirectional videos. Different from similar approaches, the binary images of vehicles obtained by background subtraction in a sequence of frames are averaged over time. We show with experiments that using the average shape of the object results in a more accurate classification than using a single frame. The vehicle types we classify are motorcycle, car and van. We created an omnidirectional video dataset and repeated experiments with shuffled train-test sets to ensure randomization.en_US
dc.description.sponsorshipDept Comp Engn & Elect & Elect Engn, Elect & Elect Engn, Bilkent Univen_US
dc.language.isoturen_US
dc.publisherIEEEen_US
dc.relation.ispartofseriesSignal Processing and Communications Applications Conference
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectOmnidirectional cameraen_US
dc.subjectOmnidirectional videoen_US
dc.subjectVehicle detectionen_US
dc.subjectVehicle classificationen_US
dc.titleClassification of Vehicles Using Binary Foreground Images Averaged Over Timeen_US
dc.typeconferenceObjecten_US
dc.typeconferenceObjecten_US
dc.relation.journal2015 23Rd Signal Processing And Communications Applications Conference (Siu)en_US
dc.contributor.departmentIzmir Isntitute of Technologyen_US
dc.identifier.startpage391en_US
dc.identifier.endpage394en_US
dc.identifier.wosWOS:000380500900076
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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