Detection of Driver Drowsiness by Calculating the Speed of Eye Blinking

TL;DR

This paper proposes a real-time driver drowsiness detection system based on eye blinking speed, using HOG and SVM for eye detection, with evaluations highlighting its effectiveness and limitations.

Contribution

The study introduces a simple, real-time drowsiness detection method based on eye blinking rate, utilizing HOG and SVM, and evaluates its minimal requirements and reliability.

Findings

System detects drowsiness when blinking speed drops below threshold

Works well with frontal face orientation

Less reliable with significant head tilts

Abstract

Many road accidents are caused by drowsiness of the driver. While there are methods to detect closed eyes, it is a non-trivial task to detect the gradual process of a driver becoming drowsy. We consider a simple real-time detection system for drowsiness merely based on the eye blinking rate derived from the eye aspect ratio. For the eye detection we use HOG and a linear SVM. If the speed of the eye blinking drops below some empirically determined threshold, the system triggers an alarm, hence preventing the driver from falling into microsleep. In this paper, we extensively evaluate the minimal requirements for the proposed system. We find that this system works well if the face is directed to the camera, but it becomes less reliable once the head is tilted significantly. The results of our evaluations provide the foundation for further developments of our drowsiness detection system.