A review on driver drowsiness based on image, bio-signal, and driver behavior
A review on driver drowsiness based on image, bio-signal, and driver behavior
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基于图像、生物信号和驾驶员行为的驾驶员困倦评价
DOI：
10.1109/icstc.2017.8011855
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
T. B. Adji
中科院分区：
文献类型：
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作者：
Bagus G. Pratama;I. Ardiyanto;T. B. Adji
由瞌睡引起的交通事故所占比例逐年略有上升。这种案件的受害者大多是年轻人，而且大多发生在发达国家。因此，为了减少因驾驶员困倦而导致的交通事故，世界各地的研究人员开发了一些自动检测驾驶员面部困倦的方法。他们提出了各种功能，如视觉，非视觉和车辆。从驾驶员的面部提取视觉特征，并由摄像机记录。非视觉特征是驾驶员身体发出的信号，为了获取这些信号，它们使用附着在驾驶员身体上的特殊传感器。车辆特征是通过观察驾驶员在行驶过程中的行为来获得的。从研究人员提出的这些特征中，我们讨论了3个可以被认为是领导研究人员开发困倦检测的指导思想。第一个想法是创建困倦面部表情数据集，因为它可以预测困倦和疲劳。第二个想法是将联合收割机视觉、非视觉和车辆特征组合成一个，以便更好地检测。最后一个是开发易于使用和用户友好的可穿戴硬件，如用于困倦检测的智能手表。
The ratio of accidents caused by drowsiness, increases slightly year by year. The most victims of this case are young adult and mostly happens in developed country. Therefore, to reduce the number of accidents caused by drowsiness, researchers around the world develope some methods for detecting drowsiness on driver's face automatically. They propose various features such as visual, non-visual, and vehicular. Visual features are extracted from driver's face and recorded by camera. Non-visual features are signals emerged from driver's body and to acquire those signals, they use special sensor attached to driver's body. Vehicular features are obtained by observing the behavior of driver during driving. From those features which are propsed by researchers, we discussed 3 ideas that can be considered as guidance to lead researcher in developing drowsiness detection. The first idea is creating the dataset of drowsiness facial expression because it can predict drowsiness and fatigue. Second idea is to combine visual, non-visual, and vehicular features into one for better detection. And last one is developing wearable hardwares such as smartwatch for drowsiness detection which are easy to use and user friendly.