基于多面部特征融合的驾驶员视觉疲劳检测

Sancharee Das, Rupal Bhargava
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引用次数: 0

摘要

据报道,疲劳驾驶是导致全球数百万人丧生的交通事故的主要原因。研究表明,大多数交通事故要么发生在夜间,要么发生在清晨,这时司机已经很疲劳,而且光线不足,无法注意到障碍物。一些自动疲劳检测系统使用生理信号,如脑电图、心电图和血压运动。但是,在大多数情况下,获取这些信号的侵入性使得它们不理想。最近开发的基于计算机视觉的疲劳检测系统过于笨重,或者由于使用单一面部特征或低光条件进行预测而具有有限的准确性。因此,提出的方法首先通过提高整体饱和度和使用伽玛校正创建均匀图像来增强低光图像。然后将增强后的图像馈送到改进的多任务级联卷积神经网络中进行人脸检测和面部地标提取。最后,将提取的眼态和口态特征输入LSTM网络进行疲劳分类。该模型的输出决定了驾驶员是疲劳还是警觉。公开可用的YawDD数据集的镜像子集已被用于对所提议的模型进行有效的训练和评估。该模型在验证集上获得了异常高的F1分数0.98和召回率0.99。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Vision-Based Fatigue Detection In Drivers Using Multi-Facial Feature Fusion
Fatigued driving has been reported to be a major cause of road accidents claiming millions of lives worldwide. Studies have shown that most road accidents occur either at night or early morning when the driver is already fatigued and there is insufficient light to notice obstacles. Some of the automated fatigue detection systems use physiological signals like EEG, ECG, and blood pressure movements. But, in most cases, the invasive nature of obtaining these signals makes them non-ideal. The recently developed computer vision based fatigue detection systems are too bulky or have limited accuracy due to prediction using single facial features or low-light conditions. Hence, the proposed method first enhances low-light images by improving the overall saturation and creating a uniform image using Gamma Correction. The enhanced images are then fed to a modified Multi-Task Cascaded Convolutional Neural Network for face detection and facial landmark extraction. Finally, the extracted eye state and mouth state features are fed to the LSTM network for fatigue classification. The output of this model decides whether the driver is fatigued or alert. The Mirror subset of the publicly available YawDD data set has been used for effective training and evaluation of the proposed model. The model achieved an exceptionally high F1 score of 0.98 and a Recall of 0.99 on the validation set.
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