Driver Fatigue Detection using Mean Intensity, SVM, and SIFT

TitleDriver Fatigue Detection using Mean Intensity, SVM, and SIFT
Publication TypeJournal Article
Year of PublicationIn Press
AuthorsNaz, S., S. Ziauddin, and A. R. Shahid
JournalInternational Journal of Interactive Multimedia and Artificial Intelligence
IssueIn Press
VolumeIn Press
NumberIn Press
Date Published10/2017

Driver fatigue is one of the major causes of accidents. This has increased the need for driver fatigue detection mechanism in the vehicles to reduce human and vehicle loss during accidents. In the proposed scheme, we capture videos from a camera mounted inside the vehicle. From the captured video, we localize the eyes using Viola-Jones algorithm. Once the eyes have been localized, they are classified as open or closed using three different techniques namely mean intensity, SVM, and SIFT. If eyes are found closed for a considerable amount of time, it indicates fatigue and consequently an alarm is generated to alert the driver. Our experiments show that SIFT outperforms both mean intensity and SVM, achieving an average accuracy of 97.45% on a dataset of five videos, each having a length of two minutes.

KeywordsDriver Fatigue Detection, Eye Detection, Scale Invariant Feature Transform, Support Vector Machine, Traffic Accidents
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