Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/4320
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dc.contributor.advisorBaştanlar, Yalınen_US
dc.contributor.authorKaraimer, Hakkı Can-
dc.date.accessioned2015-08-11T07:44:59Z
dc.date.available2015-08-11T07:44:59Z
dc.date.issued2015-06
dc.identifier.citationKaraimer, Hakkı C. (2015). Shape based detection and classification of vehicles using omnidirectional videos. Unpublished master's thesis, İzmir Institute of Technology, İzmir, Turkeyen_US
dc.identifier.urihttp://hdl.handle.net/11147/4320
dc.descriptionThesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2015en_US
dc.descriptionText in English; Abstract: Turkish and Englishen_US
dc.descriptionIncludes bibliographical references (leaves: 40-44)en_US
dc.descriptionxiii, 44 leavesen_US
dc.description.abstractTo detect and classify vehicles in omnidirectional videos, an approach based on the shape (silhouette) of the moving object obtained by background subtraction is proposed. Different from other shape based classification techniques, the information available in multiple frames of the video is exploited. Two different approaches were investigated for this purpose. One is combining silhouettes extracted from a sequence of frames to create an average silhouette, the other is making individual decisions for all frames and use consensus of these decisions. Using multiple frames eliminates most of the wrong decisions which are caused by a poorly extracted silhouette from a single video frame. The vehicle types which are classified are motorcycle, car (sedan) and van (minibus). The features extracted from the silhouettes are convexity, elongation, rectangularity, and Hu moments. Three separate methods of classification is applied. The first one is a flowchart based (i.e. rule based) method, the second one is K nearest neighbor classification, and the third one is using a Deep Neural Network. 60% of the samples in the dataset are used for training. To ensure randomization, the procedure is repeated three times with the whole dataset split each time differently into training and testing samples (i.e. three-fold cross validation). The results indicate that using silhouettes in multiple frames performs better than using single frame silhouettes.en_US
dc.description.sponsorshipThe Scientific and Technical Research Council of Turkey (TUBITAK) under the grant 113E107.en_US
dc.language.isoenen_US
dc.publisherIzmir Institute of Technologyen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectFlowchart methoden_US
dc.subjectK Nearest neighborsen_US
dc.subjectDeep neural networksen_US
dc.subjectComputer visionen_US
dc.subjectSilhouette-based methoden_US
dc.subject.lcshVehicle detectorsen_US
dc.subject.lcshElectronic traffic controlsen_US
dc.titleShape based detection and classification of vehicles using omnidirectional videosen_US
dc.title.alternativeTümyönlü videolar kullanarak şekil tabanlı araç tespiti ve sınıflandırılmasıen_US
dc.typeMaster Thesisen_US
dc.institutionauthorKaraimer, Hakkı Can-
dc.departmentThesis (Master)--İzmir Institute of Technology, Computer Engineeringen_US
dc.relation.publicationcategoryTezen_US
item.openairetypeMaster Thesis-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextopen-
Appears in Collections:Master Degree / Yüksek Lisans Tezleri
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