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Detection of driver drowsiness using multi-task cnn framework
K. Dhinakaran, N. Duraimurugan, S. Sowmiya,
Published in Science and Engineering Research Support Society
Volume: 29
Issue: 4
Pages: 1798 - 1804
Driving may be a skill that needs our full attention to safety control the vehicle and reply to event happening on the road. Distracted driving becomes a bigger threat per annum and has been the leading causes of accidents for the past decades. There are 3 forms of distractions i eyes off the road visual, ii mind off the road cognitive, iii fatigue or drowsiness while driving. in step with the study released by National Highway Traffic Safety AdministrationNHTSA and also the Virginia Tech Transportation InstituteVTTI, 80 percent of collision and 65 percent of near collision involve some kind of driver distraction. To deal with these challenges, we introduce a system to observe the motive force in terms of fatigue, distraction and activities. This method deals with automatic driver activity detection supported visual information and MTCNN. We propose an algorithm to trace and analyze drivers eyes PERCOLSpercentage of eyelid closure, heartbeat rate. © 2020 SERSC.
About the journal
JournalInternational Journal of Advanced Science and Technology
PublisherScience and Engineering Research Support Society