摘要
针对疲劳驾驶检测方法存在疲劳特征单一、鲁棒性低和不能因驾驶员不同定制疲劳阈值等问题,提出了一种基于脸部特征和头部姿态的疲劳检测方法。利用HOG(histogram of oriented gradients)特征算子和回归树算法进行人脸检测和人脸关键点定位;通过脸部关键点结合坐标系变换估计头部姿态欧拉角;建立深度残差神经网络模型对眼部疲劳特征进行提取,同时结合眼部、嘴部纵横比和头部姿态欧拉角进行疲劳特征提取;利用眼部、嘴部和头部姿态疲劳特征建立针对不同驾驶员的支持向量机模型对疲劳驾驶进行预警。实验表明:在YawDD和自建疲劳模拟数据集上,该方法均表现出较高的准确率和鲁棒性,在某一疲劳特征检测受阻时依然能进行较好的疲劳预警。
Aiming at the of the single fatigue characteristics, low robustness and inability to customize fatigue thresholds for different drivers of fatigue detection methods, a method based on facial features and head posture is proposed. In face detection and face key point positioning HOG feature operator and regression tree algorithm are used. In head posture estimation, head posture Euler angle is estimated by combining the face key points with the coordinate system transformation. In fatigue feature extraction, a deep residual neural network model is established to extract the eye fatigue features, which the eye,mouth aspect ratio and head posture Euler angle. The fatigue characteristics of eyes, mouth and head are used to establish the support vector machine models for different drivers to provide the early fatigue driving warning. Experiments show that on YawDD and self-built fatigue simulation data sets, the method shows high accuracy and robustness, and can provide better fatigue warning when a certain fatigue feature detection is blocked.
作者
陆荣秀
张笔豪
莫振龙
Lu Rongxiu;Zhang Bihao;Mo Zhenlong(School of Electrical and Automation Engineering,East China Jiaotong University,Nanchang 330013,China;Key Laboratory of Advanced Control and Optimization of Jiangxi Province,Nanchang 330013,China;School of Transportation and Logistics,East China Jiaotong University,Nanchang 330013,China)
出处
《系统仿真学报》
CAS
CSCD
北大核心
2022年第10期2279-2292,共14页
Journal of System Simulation
基金
国家自然科学基金(61733005,61863014,61963015)。
关键词
疲劳驾驶
人脸检测
头部姿态
深度学习
支持向量机
fatigue driving
face detection
head posture
deep learning
support vector machine
