CAJ | 학술논문

PERCLOS值因其良好的非接触性和准确性而被广泛应用于疲劳检测，但通常只采用一种PERCLOS标准.针对这种情况，该文提出眼睛持续闭合时间和动态 PERCLOS 值两个参数进行疲劳检测.该算法首先利用Haar-like 分类器和 Adaboost 算法进行人脸检测和定位；然后利用人脸结构特征缩小人眼的搜索区域，进一步利用 Adaboost 算法定位人眼，避免了眉毛的影响；最后采用图像形态学等图像处理方法获取人眼的垂直高度即上下眼帘的距离，判断人眼是否闭合.在疲劳预测阶段，分时间段采用不同的 PERCLOS 值标准进行判断.该算法对每秒10帧视频帧中的人眼定位准确率达到86.14%，并达到实时性要求，能够提高预测疲劳驾驶的准确性.
PERCLOS치인기량호적비접촉성화준학성이피엄범응용우피로검측，단통상지채용일충PERCLOS표준.침대저충정황，해문제출안정지속폐합시간화동태 PERCLOS 치량개삼수진행피로검측.해산법수선이용Haar-like 분류기화 Adaboost 산법진행인검검측화정위；연후이용인검결구특정축소인안적수색구역，진일보이용 Adaboost 산법정위인안，피면료미모적영향；최후채용도상형태학등도상처리방법획취인안적수직고도즉상하안렴적거리，판단인안시부폐합.재피로예측계단，분시간단채용불동적 PERCLOS 치표준진행판단.해산법대매초10정시빈정중적인안정위준학솔체도86.14%，병체도실시성요구，능구제고예측피로가사적준학성.
PERCLOS value has been used widely in drowsiness detection because of its accuracy and non-contact nature, but in practice, only one PERCLOS criterion is commonly used. In this paper, a method is proposed using continuous eye closure time and PERCLOS value simultaneously for determining the drowsiness degree. Firstly, the algorithm uses Haar-like classifier and Adaboost algorithm for face detection and localization. Then the searching area of the human eyes is narrowed based on human facial structure characteristics. Then the human eyes are positioned using Adaboost algorithm, which can avoid the influence of the eyebrows. Finally image processing methods including image morphology are used to get the vertical height of the human eye, i.e., the distance between the upper and lower eyelids, which can indicate whether the eyes are closing or not. In drowsiness prediction phase, different PERCLOS criteria are used in different time slot. With 10 frames/s testing video speed, the accuracy of the algorithm can reach 86.14%. The method presented in this paper can meet the real-time requirements and improve the accuracy of driver drowsiness degree predictions.