Driver drowsiness is one of the major causes of traffic accidents. This paper presents a nonintru- sive drowsiness recognition method using eye-tracking and image processing. A robust eye detection algo- rithm is intr...Driver drowsiness is one of the major causes of traffic accidents. This paper presents a nonintru- sive drowsiness recognition method using eye-tracking and image processing. A robust eye detection algo- rithm is introduced to address the problems caused by changes in illumination and driver posture. Six measures are calculated with percentage of eyelid closure, maximum closure duration, blink frequency, av- erage opening level of the eyes, opening velocity of the eyes, and closing velocity of the eyes. These meas- ures are combined using Fisher＇s linear discriminant functions using a stepwise method to reduce the cor- relations and extract an independent index. Results with six participants in driving simulator experiments demonstrate the feasibility of this video-based drowsiness recognition method that provided 86% accuracy.展开更多
基金Supported by the National High-Tech Research and Development (863) Program of China (No. 2009AA11Z214)Independent Scientific Research Program of Tsinghua University (No. 20101081763)
文摘Driver drowsiness is one of the major causes of traffic accidents. This paper presents a nonintru- sive drowsiness recognition method using eye-tracking and image processing. A robust eye detection algo- rithm is introduced to address the problems caused by changes in illumination and driver posture. Six measures are calculated with percentage of eyelid closure, maximum closure duration, blink frequency, av- erage opening level of the eyes, opening velocity of the eyes, and closing velocity of the eyes. These meas- ures are combined using Fisher＇s linear discriminant functions using a stepwise method to reduce the cor- relations and extract an independent index. Results with six participants in driving simulator experiments demonstrate the feasibility of this video-based drowsiness recognition method that provided 86% accuracy.
