Autors: Neshov, N. N., Manolova, A. H.
Title: Drowsiness monitoring in real-time based on Supervised Descent Method
Keywords: drosiness detection, yawning detection, Supervised Descent M

Abstract: With increased work load and unsuitable work shifts to survive in the fast paced world of today, people tend to lose sleep. Irregular sleep patterns and lack of sleep leads to drowsiness and fatigue. Drowsiness is perilous for the driver himself and for other drivers on the road and must be avoided for example by noise alerts in the car. This paper describes a method to detect drowsiness after implementing eye-tracking and mouth shape tracking in real-time. Viola-Jones algorithm is used to detect facial features in real-time. The proposed approach uses the detected facial features (i.e. eyes and mouth) based on Supervised Descent Method to find the blinking rate of a driver as well as for yawning detection. A decision, whether the driver is vigilant or not is then provided. Real time experiments based on publicly available dataset prove that the proposed method is highly efficient in finding the drowsiness and alerting the driver.

References

    Issue

    Proceedings of the 2017 IEEE 9th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2017, vol. 2, issue 3, pp. 660-663, 2017, Romania, IEEE, DOI 10.1109/IDAACS.2017.8095173

    Copyright IEEE

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    Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus и Web of Science