Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/8619
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dc.contributor.authorAli N.-
dc.contributor.authorHasan I.-
dc.contributor.authorÖzyer, Tansel-
dc.contributor.authorAlhajj R.-
dc.date.accessioned2022-07-30T16:43:35Z-
dc.date.available2022-07-30T16:43:35Z-
dc.date.issued2021-
dc.identifier.citationAli, N., Hasan, I., Özyer, T., & Alhajj, R. (2021, December). Driver Drowsiness Detection by Employing CNN and Dlib. In 2021 22nd International Arab Conference on Information Technology (ACIT) (pp. 1-5). IEEE.en_US
dc.identifier.isbn9781665419956-
dc.identifier.urihttps://doi.org/10.1109/ACIT53391.2021.9677197-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/8619-
dc.description22nd International Arab Conference on Information Technology, ACIT 2021 -- 21 December 2021 through 23 December 2021 -- -- 176492en_US
dc.description.abstractEvery year thousands of people lose their life due to road accidents. One of the main reasons for these accidents is driver drowsiness. In driver drowsiness, the driver slept while driving, which causes the road accident, especially on the long routes. Driver fatigue and micro sleep while driving caused the fatal accident and death of human beings. To overcome this problem, we are implementing a technique in which we capture the image of the driver. After capturing the image of the driver, we process driver images to detect driver drowsiness. For the processing of the driver image, we are using two different techniques with each other. In the first technique, we are using the Dlib for image drowsiness detection by detecting that driver’s eyes are closed and the driver is yawning. In the second technique, we used CNN for the detection of yawning and the eyes of the driver are closed or not and predict driver drowsiness. After implementing the two techniques we combine the output of both techniques. After combining both techniques we test the system, and it gives us very good results. © 2021 IEEE.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2021 22nd International Arab Conference on Information Technology, ACIT 2021en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCNNen_US
dc.subjectDliben_US
dc.subjectDriver drowsinessen_US
dc.subjectPytorchen_US
dc.subjectResneten_US
dc.subjectHighway accidentsen_US
dc.subjectImage processingen_US
dc.subjectCNNen_US
dc.subjectDliben_US
dc.subjectDriver drowsinessen_US
dc.subjectDriver fatigueen_US
dc.subjectDrowsiness detectionen_US
dc.subjectFatal accidentsen_US
dc.subjectHuman beingen_US
dc.subjectMicrosleepen_US
dc.subjectPytorchen_US
dc.subjectResneten_US
dc.subjectRoads and streetsen_US
dc.titleDriver Drowsiness Detection by Employing Cnn and Dliben_US
dc.typeConference Objecten_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.identifier.scopus2-s2.0-85125335901en_US
dc.institutionauthorÖzyer, Tansel-
dc.identifier.doi10.1109/ACIT53391.2021.9677197-
dc.authorscopusid57650787800-
dc.authorscopusid57468974200-
dc.authorscopusid8914139000-
dc.authorscopusid7004187647-
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.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
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