摘要
眼睛和嘴部状态检测是疲劳检测方法的重要步骤,但眼镜遮挡及光照变化使得眼睛状态识别效果不佳。为此,提出一种新的驾驶员疲劳检测方法。使用红外采集设备对驾驶员面部图像进行采集,通过结合AdaBoost与核相关滤波器算法进行人脸检测及跟踪。采用级联回归方法定位特征点,提取眼睛和嘴部区域。运用卷积神经网络进行眼睛和嘴部状态识别,在此基础上计算多个疲劳参数进行疲劳检测。实验结果表明,该方法在多种情况下均能准确地检测眼睛和嘴部状态,可有效地进行疲劳检测。
The state detection method of eye and mouth is the key issue for fatigue detection,but it is affected by changing of illumination and wearing glasses. To solve above problems, a fatigue detection method based on facial behavior analysis is proposed. It designs an infrared video acquisition system for driver. The driver＇ s face is detected based on AdaBoost and the Kernelized Correlation Filter（KCF） tracking algorithm. The feature points are determined by the method of cascade regression, and the eye and mouth regions are obtained. Convolution Neural Network（CNN） is utilized to detect the state of eye and mouth. On this basis, the fatigue parameters are calculated for fatigue detection. Experimental results show that the method can detect the state of eye and mouth accurately and detect fatigue more effectively in many circumstances.
出处
《计算机工程》
CAS
CSCD
北大核心
2018年第1期274-279,共6页
Computer Engineering
基金
国家自然科学基金(61601325)
天津市科技支撑计划重点项目(14ZCZDGX00033)
天津市科技特派员项目(15JCTPJC56300)
关键词
疲劳检测
人脸检测
特征点检测
状态识别
核相关滤波器
卷积神经网络
fatigue detection
face detection
feature point detection
state recognition
Kernelized Correlation Filter（KCF）
Convolution Neural Network （CNN）
