Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/2016
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSarıkan, Selim S.-
dc.contributor.authorÖzbayoğlu, Ahmet Murat-
dc.date.accessioned2019-07-10T14:42:46Z
dc.date.available2019-07-10T14:42:46Z
dc.date.issued2018
dc.identifier.citationSarikan, S. S., & Ozbayoglu, A. M. (2018). Anomaly Detection in Vehicle Traffic with Image Processing and Machine Learning. Procedia Computer Science, 140, 64-69.en_US
dc.identifier.issn18770509
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S1877050918319665?via%3Dihub-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/2016-
dc.descriptionComplex Adaptive Systems Conference with Theme: Cyber Physical Systems and Deep Learning (2018 : United States)
dc.description.abstractAnomaly detection is an important part of an Intelligent Transportation System. In this study, image processing and machine learning techniques are used to detect anomalies in vehicle movements. These anomalies include standing and traveling in reverse direction. Images are captured using CCTV cameras from front and rear side of the vehicle. This capability makes the results robust to the variations in operational and environmental conditions. Multiple consecutive frames are acquired for motion detection. Features such as edges and license plate corner locations are extracted for tracking purposes. Direction of the traffic flow is obtained from the trained classifier. K-nearest neighbor is chosen as the classifier model. The proposed method is evaluated on a public highway and promising detection results are achieved. © 2018 The Authors. Published by Elsevier B.V.en_US
dc.language.isoenen_US
dc.publisherElsevier B.V.en_US
dc.relation.ispartofProcedia Computer Scienceen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectVehiclesen_US
dc.subjectClassification (of information)en_US
dc.subjectvehicle logoen_US
dc.titleAnomaly Detection in Vehicle Traffic With Image Processing and Machine Learningen_US
dc.typeConference Objecten_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.volume140
dc.identifier.startpage64
dc.identifier.endpage69
dc.authorid0000-0001-7998-5735-
dc.identifier.wosWOS:000547349000008en_US
dc.identifier.scopus2-s2.0-85061961476en_US
dc.institutionauthorÖzbayoğlu, Ahmet Murat-
dc.identifier.doi10.1016/j.procs.2018.10.293-
dc.authorwosidH-2328-2011-
dc.authorscopusid6505999525-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.scopusquality--
item.openairetypeConference Object-
item.languageiso639-1en-
item.grantfulltextopen-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.1. Department of Artificial Intelligence Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Files in This Item:
File Description SizeFormat 
ozbayoglu-Anomaly.pdf1.64 MBAdobe PDFThumbnail
View/Open
Show simple item record



CORE Recommender

SCOPUSTM   
Citations

8
checked on Dec 21, 2024

WEB OF SCIENCETM
Citations

19
checked on Dec 21, 2024

Page view(s)

122
checked on Dec 16, 2024

Download(s)

90
checked on Dec 16, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.