Spatio-temporal features for the automatic control of driver drowsiness state and lack of concentration

Abstract

Driver fatigue is one of the leading causes of road accidents. It affects the mental vigilance of the driver and reduces his personal capacity to drive a vehicle in full safety. These factors increase the risk of human errors which could involve deaths and wounds. Consequently, the development of an automatic system, which controls the driver fatigue and prevents him from accidents in advance, has received a growing interest. In this work, we have proposed a fusion system for drowsiness detection based on blinking measurement and the 3D head pose estimation. We have studied the driver's eye behaviors by analysing a non-stationary and non-linear signal and we estimate the head rotation in the three directions $$Yaw$$Yaw, $$Pitch$$Pitch, and $$Roll$$Roll by exploiting only three interest points of the face. Our suggested system of fusion presents three levels of drowsiness: awake, tired, and very tired. This system is evaluated by both $$DEAP$$DEAP and $$MiraclHB$$MiraclHB databases. The evaluation shows many promising results and shows the effectiveness of the suggested approach.