Real time driver fatigue detection based on SVM algorithm. Paper presented at the 6th International Conference on Control Engineering & Information Technology (CEIT), Istanbul, Turkey, 25-27 October 2018.

Author(s)
Kir Savas, B. & Becerikli, Y.
Year
Abstract

Among the causes of the traffic accidents driver drowsiness comes at one of the first places. According to the literature some work has been done in driver fatigue detection. This paper proposes a real time driver fatigue detection based on Support Vector Machine (SVM) algorithm. Fatigue detection mainly focuses on drivers' face expressions and behaviors. OpenCV and Dlib libraries were utilized to detect the expressions of drivers' faces. The proposed system has five stages: PERCLOS, count of yawn, internal zone of the mouth opening, count of eye blinking and head detection to extract attributes from real time video. Subsequently, facial expressions were trained with SVM. In this study an SVM-based driver fatigue detection is recommended and the tests showed that the accuracy rate of fatigue detection is up to 97.93%. (Author/publisher)

Request publication

3 + 2 =
Solve this simple math problem and enter the result. E.g. for 1+3, enter 4.

Publication

Library number
20210536 ST [electronic version only]
Source

In: Proceedings of the 6th International Conference on Control Engineering & Information Technology (CEIT), Istanbul, Turkey, 25-27 October 2018, [4] p., 18 ref.

Our collection

This publication is one of our other publications, and part of our extensive collection of road safety literature, that also includes the SWOV publications.