Automated Detection of Driver Fatigue Based on Entropy and Complexity Measures
Fatigue driving is a major cause for traffic accidents. One of the most effective ways to detect fatigue driving is by machine vision based on driver's eye fatigue features. However, it is highly affected by lighting conditions in real driving conditions. This paper firstly corrects the color balance of video images to extract the lighting-adaptive skin color features. Then the eye area is clipped out with SURF features to extract the time-space features of fatigue drivers. This makes fatigue detecting become robust under complex real driving conditions. Experiments show that this method possesses strong adaptability to lighting conditions, and thus better generalization in engineering.