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.

Auteur(s)
Kir Savas, B. & Becerikli, Y.
Jaar
Samenvatting

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)

Publicatie aanvragen

1 + 16 =
Los deze eenvoudige rekenoefening op en voer het resultaat in. Bijvoorbeeld: voor 1+3, voer 4 in.

Publicatie

Bibliotheeknummer
20210536 ST [electronic version only]
Uitgave

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

Onze collectie

Deze publicatie behoort tot de overige publicaties die we naast de SWOV-publicaties in onze collectie hebben.