Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/6476
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dc.contributor.authorKaraimer, Hakkı Can-
dc.contributor.authorBarış, İpek-
dc.contributor.authorBaştanlar, Yalın-
dc.date.accessioned2017-11-17T07:11:53Z-
dc.date.available2017-11-17T07:11:53Z-
dc.date.issued2017-08-
dc.identifier.citationKaraimer, H. C., Barış, İ., and Baştanlar, Y. (2017). Detection and classification of vehicles from omnidirectional videos using multiple silhouettes. Pattern Analysis and Applications, 20(3), 893-905. doi:10.1007/s10044-017-0593-zen_US
dc.identifier.issn1433-7541-
dc.identifier.issn1433-755X-
dc.identifier.urihttp://doi.org/10.1007/s10044-017-0593-z-
dc.identifier.urihttp://hdl.handle.net/11147/6476-
dc.description.abstractTo detect and classify vehicles in omnidirectional videos, we propose an approach based on the shape (silhouette) of the moving object obtained by background subtraction. Different from other shape-based classification techniques, we exploit the information available in multiple frames of the video. We investigated two different approaches 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 we classify are motorcycle, car (sedan) and van (minibus). The features extracted from the silhouettes are convexity, elongation, rectangularity and Hu moments. We applied two separate methods of classification. First one is a flowchart-based method that we developed and the second is K-nearest neighbour classification. 60% of the samples in the dataset are used for training. To ensure randomization in the experiments, threefold cross-validation is applied. The results indicate that using multiple silhouettes increases the classification performance.en_US
dc.language.isoenen_US
dc.publisherSpringer Verlagen_US
dc.relation.ispartofPattern Analysis and Applicationsen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectObject detectionen_US
dc.subjectOmnidirectional camerasen_US
dc.subjectTraffic surveillanceen_US
dc.subjectVehicle classificationen_US
dc.subjectVehicle detectionen_US
dc.titleDetection and classification of vehicles from omnidirectional videos using multiple silhouettesen_US
dc.typeArticleen_US
dc.authoridTR176747en_US
dc.departmentIzmir Institute of Technology. Computer Engineeringen_US
dc.identifier.volume20en_US
dc.identifier.issue3en_US
dc.identifier.startpage893en_US
dc.identifier.endpage905en_US
dc.identifier.wosWOS:000405607000020
dc.identifier.scopusSCOPUS:2-s2.0-85011797326
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1007/s10044-017-0593-z-
dc.relation.doi10.1007/s10044-017-0593-zen_US
dc.coverage.doi10.1007/s10044-017-0593-zen_US
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en-
item.fulltextWith Fulltext-
item.cerifentitytypePublications-
item.grantfulltextopen-
crisitem.author.deptDepartment 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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