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dc.contributor.authorZhu, T
dc.contributor.authorZhang, C
dc.contributor.authorWu, T
dc.contributor.authorOuyang, Z
dc.contributor.authorLi, H
dc.contributor.authorNa, X
dc.contributor.authorLiang, J
dc.contributor.authorLi, W
dc.date.accessioned2022-02-23T03:30:09Z
dc.date.available2022-02-23T03:30:09Z
dc.date.issued2022-02-21
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/334361
dc.description.abstract<jats:p>The research on driver fatigue detection is of great significance to improve driving safety. This paper proposes a real-time comprehensive driver fatigue detection algorithm based on facial landmarks to improve the detection accuracy, which detects the driver’s fatigue status by using facial video sequences without equipping their bodies with other intelligent devices. A tasks-constrained deep convolutional network is constructed to detect the face region based on 68 key points, which can solve the optimization problem caused by the different convergence speeds of each task. According to the real-time facial video images, the eye feature of the eye aspect ratio (EAR), mouth aspect ratio (MAR) and percentage of eye closure time (PERCLOS) are calculated based on facial landmarks. A comprehensive driver fatigue assessment model is established to assess the fatigue status of drivers through eye/mouth feature selection. After a series of comparative experiments, the results show that this proposed algorithm achieves good performance in both accuracy and speed for driver fatigue detection.</jats:p>
dc.languageen
dc.publisherMDPI AG
dc.subjectdriver fatigue detection
dc.subjecttask-constrained deep convolutional network
dc.subjectfacial landmarks
dc.titleResearch on a Real-Time Driver Fatigue Detection Algorithm Based on Facial Video Sequences
dc.typeArticle
dc.date.updated2022-02-23T03:30:08Z
prism.issueIdentifier4
prism.publicationNameApplied Sciences (Switzerland)
prism.volume12
dc.identifier.doi10.17863/CAM.81774
dcterms.dateAccepted2022-02-17
rioxxterms.versionofrecord10.3390/app12042224
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidNa, Xiaoxiang [0000-0002-6524-7122]
dc.identifier.eissn2076-3417
pubs.funder-project-idTeaching Quality and Reform of Higher Vocational Education Project of Guangdong Province (GDJG2019463, 2019KTSCX201, 2020SN004, zlgc202034, 2021ZDZX1061)
cam.issuedOnline2022-02-21


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