Drowsiness monitoring in real-time based on supervised descent method

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.

• Nikolay Neshov
• Agata Manolova

9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), 2017.

https://ieeexplore.ieee.org/document/8095173