CAJ | 학술논문

为了监测驾驶员的疲劳状态，提出了一种基于面部多种疲劳参数的驾驶员状态检测算法。首先利用Gabor滤波和梯度信息增强眼睛和嘴部的边缘信息以进行准确定位，然后采用一种旋转不变的LBP金字塔特征对眼睛进行特征描述，训练线性SVM分类器判别眼睛的开闭状态；并根据嘴部的张开面积及宽高比判断嘴部的开闭状态，同时通过统计眼睛在垂直方向上的运动确定头部位置的变化。最后基于眼睛和嘴部的状态、头部的位置，计算出4个能够描述驾驶员状态的疲劳参数，利用模糊系统推理得出驾驶员最终的疲劳状态。实验结果证明检测和状态判别的算法都有较高的准确率，其中眼睛状态的识别率平均在97％，嘴部状态的识别率也能达到92％；模糊系统的合理性也在实验中得以验证。
위료감측가사원적피로상태，제출료일충기우면부다충피로삼수적가사원상태검측산법。수선이용Gabor려파화제도신식증강안정화취부적변연신식이진행준학정위，연후채용일충선전불변적LBP금자탑특정대안정진행특정묘술，훈련선성SVM분류기판별안정적개폐상태；병근거취부적장개면적급관고비판단취부적개폐상태，동시통과통계안정재수직방향상적운동학정두부위치적변화。최후기우안정화취부적상태、두부적위치，계산출4개능구묘술가사원상태적피로삼수，이용모호계통추리득출가사원최종적피로상태。실험결과증명검측화상태판별적산법도유교고적준학솔，기중안정상태적식별솔평균재97％，취부상태적식별솔야능체도92％；모호계통적합이성야재실험중득이험증。
In order to detect the drowsiness state of the driver , a detection algorithm which is based on multi-parame-ters of facial drowsiness is proposed.Firstly, the marginal information of the eyes and the mouth are enhanced by using Gabor filter and gradient information to localize them precisely , and then the state of the eyes is classified as being open and closed by a linear SVM classifier trained by an rotation-invariant LBP features of the eye image , and the state of the mouth is determined according to the opening-area and the ratio of width-height of the mouth .And meanwhile the change of the head position is confirmed by the statistics of the movement of the eyes in the vertical o-rientation.Finally, the state of the driver can be reasoned by the fuzzy system using four drowsiness ’ s coefficients calculated by the state of the eyes and the mouth , and the head position.The experiment results prove that the detec-tion and the state discrimination algorithm have higher accuracy , of which the average accuracy of the recognition of the eye state amounts to 97%, and the recognition of mouth state can also reach to 92%.The reasonability of the fuzzy system is verified in the experiment as well .