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dc.contributor.authorVaca-Recalde, Myriam E.
dc.contributor.authorPérez, Joshué
dc.contributor.authorEchanobe, Javier
dc.date.accessioned2020-12-15T10:45:52Z
dc.date.available2020-12-15T10:45:52Z
dc.date.issued2020-10-27
dc.identifier.citationVaca-Recalde, Myriam E., Joshué Pérez, and Javier Echanobe. “Driver Monitoring System Based on CNN Models: An Approach for Attention Level Detection.” Intelligent Data Engineering and Automated Learning – IDEAL 2020 (2020): 575–583. doi:10.1007/978-3-030-62365-4_56.en
dc.identifier.isbn978-3-030-62365-4; 978-3-030-62364-7en
dc.identifier.issn0302-9743en
dc.identifier.urihttp://hdl.handle.net/11556/1031
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.language.isoengen
dc.publisherSpringeren
dc.titleDriver Monitoring System Based on CNN Models: An Approach for Attention Level Detectionen
dc.typeconferenceObjecten
dc.identifier.doi10.1007/978-3-030-62365-4_56en
dc.rights.accessRightsembargoedAccessen
dc.subject.keywordsDriver Monitoring Systemen
dc.subject.keywordsConvolution Neural Networken
dc.subject.keywordsArtificial Intelligenceen
dc.subject.keywordsAdvanced Driver Assistance System (ADAS)en
dc.identifier.essn1611-3349en
dc.journal.titleLecture Notes in Computer Science book seriesen
dc.page.final583en
dc.page.initial575en
dc.volume.number12490en
dc.conference.titleIntelligent Data Engineering and Automated Learning – IDEAL 2020en


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