Detection of Driver Drowsiness by Calculating the Speed of Eye Blinking
This addresses road safety by preventing accidents due to driver drowsiness, but it is an incremental improvement on existing eye detection methods.
The paper tackles driver drowsiness detection by monitoring eye blinking speed using eye aspect ratio, finding the system works well when the face is directed to the camera but becomes less reliable with significant head tilts.
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.