A novel approach in analyzing traffic flow by extreme learning machine method

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dc.authorid00001-9303-1735en_US
dc.contributor.authorSönmez, Yasin
dc.contributor.authorKutlu, Hüseyin
dc.contributor.authorAvcı, Engin
dc.date.accessioned2021-09-17T05:43:04Z
dc.date.available2021-09-17T05:43:04Z
dc.date.issued2019en_US
dc.departmentDicle Üniversitesi, Diyarbakır Teknik Bilimler Meslek Yüksekokulu, Bilgisayar Teknolojileri Bölümüen_US
dc.description.abstractThe objective of this study is to detect abnormal behaviours of moving objects captured in highway traffic flow footages, classify them by using artificial learning methods, and lastly to predict the future thereof (regression). To this end, the system being the object of the design and application consists of three stages. In the first stage, to detect the moving object in the video, background/foreground segmentation method of Mixture of Gaussian (MOG), and to track the moving object, Kalman Filter-Hungarian algorithm method have been used. In the second stage, by using the coordinates of the object, such details as location, distance in terms of time, and speed of the object are obtained, and by using total pixel count data relating to the shape of the object are obtained. The software based on the specifically elaborated algorithm compares these data with the data in the table of rules set down for the road under surveillance, and generates an attribute table comprising anomalies of the objects in the video. In the last stage, however, the data included in the attribute table have been classified and predictions by the artificial learning method, Extreme Learning Machine (ELM) made.en_US
dc.identifier.citationSönmez, Y., Kutlu, H. ve Avcı, E. (2019). A novel approach in analyzing traffic flow by extreme learning machine method. Tehnicki Vjesnik, 26(1), 107-113.en_US
dc.identifier.doi10.17559/TV-20171128220125
dc.identifier.endpage113en_US
dc.identifier.issn1330-3651
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85063457150
dc.identifier.scopusqualityQ3
dc.identifier.startpage107en_US
dc.identifier.urihttps://hrcak.srce.hr/217094
dc.identifier.urihttps://hdl.handle.net/11468/7655
dc.identifier.volume26en_US
dc.identifier.wosWOS:000458827900016
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorSönmez, Yasin
dc.language.isoenen_US
dc.publisherStrojarski Faculteten_US
dc.relation.ispartofTehnicki Vjesnik
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAnomaly classification and prediction (regression)en_US
dc.subjectArtificial learningen_US
dc.subjectExtreme learning machine (ELM)en_US
dc.subjectTraffic flow video analysisen_US
dc.titleA novel approach in analyzing traffic flow by extreme learning machine methoden_US
dc.titleA novel approach in analyzing traffic flow by extreme learning machine method
dc.typeArticleen_US

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