不同姿态下基于多特征融合的疲劳状态检测方法

针对传统的基于单一特征的疲劳检测方法误检率高、可靠性不强、无法适应复杂多变的行车环境等问题,提出了一种将驾驶员的眼睛、嘴巴等多种面部特征进行融合的疲劳驾驶检测方法。与现有的人脸检测模型相比,这里提出的基于梯度提高的学习框架对于侧脸的检测效果更佳,并且能够更好地满足检测时间上的要求;同时通过改进的LeNet-5神经网络模型对视频中的笑容进行分类,排除了表情变化对疲劳驾驶检测的干扰;最后为了降低头部姿态的偏转对疲劳特征提取的影响,引入了基于欧拉角的特征校正算法;对YawDD疲劳驾驶数据集的检测结果表明:不同姿态下基于多特征融合的疲劳驾驶检测不仅能够有效降低头部偏转对疲劳驾驶检测的影响,而且比传统的疲劳检测方法具备更高的鲁棒性。

Fatigue State Detection Method Based on Multi-feature Fusion under Different Postures

Aiming at solving the problems of high false detection rate and low reliability of traditional single feature-based fatigue detection methods, which cannot adapt to the complex and changing driving environment, a fatigue driving detection method that fuses drivers multiple facial features such as driver''s eyes and mouth is proposed. Compared with the existing face detection models, the gradient-based learning framework proposed here is more effective for side face detection and can better meet the detection time requirements. Meanwhile, the improved LeNet-5 neural network model is used to classify the smiles in videos, which excludes the interference of expression changes on fatigue driving detection. Finally, in order to reduce the influence of head posture deflection on fatigue feature extraction, the Euler angle-based feature correction algorithm was introduced. The detection results of the YawDD fatigue driving dataset show that the fatigue driving detection based on multi-feature fusion in different postures can not only effectively reduce the influence of head deflection on fatigue driving detection, but also has higher robustness than the traditional fatigue detection methods.