Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/10380
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dc.contributor.authorKarahan, M.-
dc.contributor.authorKurt, H.-
dc.contributor.authorKasnakoğlu, C.-
dc.date.accessioned2023-04-16T10:02:10Z-
dc.date.available2023-04-16T10:02:10Z-
dc.date.issued2022-
dc.identifier.isbn9781665470131-
dc.identifier.urihttps://doi.org/10.1109/ISMSIT56059.2022.9932687-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/10380-
dc.description6th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2022 -- 20 October 2022 through 22 October 2022 -- 184355en_US
dc.description.abstractDetection and counting of moving objects and recognition of license plates are important processes in traffic surveillance. In this study, the development of moving object detection and counting and license plate recognition algorithms are explained. Gaussian mixture models are used to detect, track and count the moving objects in a video sequence. The algorithm shows the total number of moving objects on the left corner of the processed video frame. Prewitt operator and optical character recognition are used to recognize vehicle's license plate. License plate recognition algorithm can recognize license plates of different types of vehicles in different positions without any character limit. Moving object detection and counting algorithm is tested using different videos of moving objects. Then, license plate recognition algorithm is tested using various photos of the different types of vehicles. It could be evaluated that moving object detection and counting algorithm easily detects and counts the moving objects and vehicle license plate recognition algorithm clearly recognizes license plates of the cars. © 2022 IEEE.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofISMSIT 2022 - 6th International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedingsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectblob analysisen_US
dc.subjectforeground detectionen_US
dc.subjectGaussian mixture modelen_US
dc.subjectlicense plate recognitionen_US
dc.subjectmoving object detectionen_US
dc.subjectoptical character recognitionen_US
dc.subjectPrewitt operatoren_US
dc.subjectGaussian distributionen_US
dc.subjectImage segmentationen_US
dc.subjectLicense plates (automobile)en_US
dc.subjectObject detectionen_US
dc.subjectObject recognitionen_US
dc.subjectBlob analysisen_US
dc.subjectForeground detectionen_US
dc.subjectGaussian Mixture Modelen_US
dc.subjectLicenses plate recognitionen_US
dc.subjectMoving objectsen_US
dc.subjectMoving-object detectionen_US
dc.subjectObject countingen_US
dc.subjectPrewitt operatoren_US
dc.subjectRecognition algorithmen_US
dc.subjectVehicle license plate recognitionen_US
dc.subjectOptical character recognitionen_US
dc.titleMoving Object Detection and Counting in Traffic With Gaussian Mixture Models and Vehicle License Plate Recognition With Prewitt Methoden_US
dc.typeConference Objecten_US
dc.departmentTOBB ETÜen_US
dc.identifier.startpage32en_US
dc.identifier.endpage36en_US
dc.identifier.scopus2-s2.0-85142845081en_US
dc.institutionauthor-
dc.identifier.doi10.1109/ISMSIT56059.2022.9932687-
dc.authorscopusid57216759940-
dc.authorscopusid57189350201-
dc.authorscopusid24802064500-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
item.openairetypeConference Object-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept03.14. Department of Internal Medicine-
crisitem.author.dept02.5. Department of Electrical and Electronics Engineering-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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