Driver Monitoring System Based on CNN Models: An Approach for Attention Level Detection: An Approach for Attention Level Detection

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dc.contributor.authorVaca-Recalde, Myriam E.
dc.contributor.authorPérez, Joshué
dc.contributor.authorEchanobe, Javier
dc.contributor.editorAnalide, Cesar
dc.contributor.editorNovais, Paulo
dc.contributor.editorCamacho, David
dc.contributor.editorYin, Hujun
dc.contributor.institutionCCAM
dc.date.issued2020-10-27
dc.descriptionPublisher Copyright: © 2020, Springer Nature Switzerland AG.
dc.description.abstractDrivers provide a wide range of focus characteristics that can evaluate their attention level and analyze their behavioral states while driving. This information is critical for the development of new automated driving functionalities that support and assist the driver according to his/her state, ensuring safety for them and other users on the road. In this sense, this paper proposes a Driver Monitoring System (DMS) based on image processing and Convolutional Neural Networks (CNN), that analyzes two important driver distraction aspects: inattention of the road and drowsiness. Our approach makes use of CNN models for detecting the gaze and the head direction, which involves training datasets with different pre-defined labels. Additionally, the system is complemented with the drowsiness level measurement, using face features to detect the time that the eyes are closed or opened, and the blinking rate. Crossing the inference results of these models, the system can provide an accurate estimation of driver attention level. The different parts of the presented DMS have been trained in a Hardware-in-the-loop driving simulator with an eye fish camera. It has been tested as a real-time application recording driver with different characteristics.en
dc.description.statusPeer reviewed
dc.format.extent9
dc.format.extent2078721
dc.identifier.citationVaca-Recalde , M E , Pérez , J & Echanobe , J 2020 , Driver Monitoring System Based on CNN Models: An Approach for Attention Level Detection : An Approach for Attention Level Detection . in C Analide , P Novais , D Camacho & H Yin (eds) , unknown . vol. 12490 , 0302-9743 , Springer , pp. 575-583 , 21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020 , Guimaraes , Portugal , 4/11/20 . https://doi.org/10.1007/978-3-030-62365-4_56
dc.identifier.citationconference
dc.identifier.doi10.1007/978-3-030-62365-4_56
dc.identifier.isbn978-3-030-62365-4; 978-3-030-62364-7
dc.identifier.isbn9783030623647
dc.identifier.otherresearchoutputwizard: 11556/1031
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85097172940&partnerID=8YFLogxK
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofunknown
dc.relation.ispartofseries0302-9743
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsDriver Monitoring System
dc.subject.keywordsConvolution Neural Network
dc.subject.keywordsArtificial Intelligence
dc.subject.keywordsAdvanced Driver Assistance System (ADAS)
dc.subject.keywordsDriver Monitoring System
dc.subject.keywordsConvolution Neural Network
dc.subject.keywordsArtificial Intelligence
dc.subject.keywordsAdvanced Driver Assistance System (ADAS)
dc.subject.keywordsTheoretical Computer Science
dc.subject.keywordsGeneral Computer Science
dc.subject.keywordsSDG 3 - Good Health and Well-being
dc.titleDriver Monitoring System Based on CNN Models: An Approach for Attention Level Detection: An Approach for Attention Level Detectionen
dc.typeconference output
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