Publication:
Driver Drowsiness Detection by Applying Deep Learning Techniques to Sequences of Images

Simple item page
dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Laboratorio de Control, Aprendizaje y Optimización de Sistemas (CAOS)es
dc.contributor.authorMagan Lopez, Elena
dc.contributor.authorSesmero Lorente, María Paz
dc.contributor.authorAlonso Weber, Juan Manuel
dc.contributor.authorSanchis de Miguel, María Araceli
dc.contributor.funderComunidad de Madrides
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.contributor.funderMinisterio de Ciencia, Innovación y Universidades (España)es
dc.date.accessioned2022-06-27T08:05:06Z
dc.date.available2022-06-27T08:05:06Z
dc.date.issued2022-02
dc.description.abstractThis work presents the development of an ADAS (advanced driving assistance system) focused on driver drowsiness detection, whose objective is to alert drivers of their drowsy state to avoid road traffic accidents. In a driving environment, it is necessary that fatigue detection is performed in a non-intrusive way, and that the driver is not bothered with alarms when he or she is not drowsy. Our approach to this open problem uses sequences of images that are 60 s long and are recorded in such a way that the subject’s face is visible. To detect whether the driver shows symptoms of drowsiness or not, two alternative solutions are developed, focusing on the minimization of false positives. The first alternative uses a recurrent and convolutional neural network, while the second one uses deep learning techniques to extract numeric features from images, which are introduced into a fuzzy logic-based system afterwards. The accuracy obtained by both systems is similar: around 65% accuracy over training data, and 60% accuracy on test data. However, the fuzzy logic-based system stands out because it avoids raising false alarms and reaches a specificity (proportion of videos in which the driver is not drowsy that are correctly classified) of 93%. Although the obtained results do not achieve very satisfactory rates, the proposals presented in this work are promising and can be considered a solid baseline for future works.en
dc.description.sponsorshipThis work was supported by the Spanish Government under projects PID2019- 104793RB-C31, TRA2016-78886-C3-1-R, RTI2018-096036-B-C22, PEAVAUTO-CM-UC3M and by the Region of Madrid’s Excellence Program (EPUC3M17).en
dc.format.extent25
dc.identifier.bibliographicCitationMagán, E., Sesmero, M. P., Alonso-Weber, J. M., & Sanchis, A. (2022). Driver Drowsiness Detection by Applying Deep Learning Techniques to Sequences of Images. In Applied Sciences, 12(3), 1145-1170en
dc.identifier.doihttps://doi.org/10.3390/app12031145
dc.identifier.issn2076-3417
dc.identifier.publicationfirstpage1145
dc.identifier.publicationissue3
dc.identifier.publicationlastpage1170
dc.identifier.publicationtitleApplied Sciences (Switzerland)en
dc.identifier.publicationvolume12
dc.identifier.urihttps://hdl.handle.net/10016/35290
dc.identifier.uxxiAR/0000030944
dc.language.isoengen
dc.publisherMDPI AGen
dc.relation.projectIDGobierno de España. TRA2016-78886-C3-1-Res
dc.relation.projectIDGobierno de España. RTI2018-096036-B-C22es
dc.relation.projectIDComunidad de Madrid. PEAVAUTO-CM-UC3Mes
dc.relation.projectIDGobierno de España. PID2019-104793RB-C31es
dc.relation.projectIDComunidad de Madrid. EPUC3M17es
dc.rights© 2022 by the authors. Licensee MDPI, Basel, Switzerland.en
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaInformáticaes
dc.subject.otherAdasen
dc.subject.otherDrowsinessen
dc.subject.otherDeep learningen
dc.subject.otherConvolutional neural networksen
dc.subject.otherRecurrent neural networksen
dc.subject.otherFuzzy logicen
dc.subject.otherComputer visionen
dc.titleDriver Drowsiness Detection by Applying Deep Learning Techniques to Sequences of Imagesen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Driver_AS_2022.pdf
Size:
2.99 MB
Format:
Adobe Portable Document Format
Download
Collections
DI - CAOS - Artículos de Revistas