PDF(Pubmed)
Abstract：
Drowsiness-related car accidents continue to have a significant effect on road safety. Many of these accidents can be eliminated by alerting the drivers once they start feeling drowsy. This work presents a non-invasive system for real-time driver drowsiness detection using visual features. These features are extracted from videos obtained from a camera installed on the dashboard. The proposed system uses facial landmarks and face mesh detectors to locate the regions of interest where mouth aspect ratio, eye aspect ratio, and head pose features are extracted and fed to three different classifiers: random forest, sequential neural network, and linear support vector machine classifiers. Evaluations of the proposed system over the National Tsing Hua University driver drowsiness detection dataset showed that it can successfully detect and alarm drowsy drivers with an accuracy up to 99%.
摘要：
与困倦有关的车祸继续对道路安全产生重大影响。一旦驾驶员开始感到困倦，就可以通过警告驾驶员来消除其中的许多事故。这项工作提出了一种使用视觉特征进行实时驾驶员困倦检测的非侵入性系统。这些特征是从从安装在仪表板上的摄像机获得的视频中提取的。所提出的系统使用面部标志和面部网格检测器来定位感兴趣的区域，其中嘴纵横比，眼睛长宽比,和头部姿态特征被提取并馈送到三个不同的分类器:随机森林,序贯神经网络，和线性支持向量机分类器。在国家清华大学驾驶员困倦检测数据集上对拟议系统的评估表明，它可以成功地检测和警告困倦驾驶员，准确率高达99％。
