Drowsiness detection using behavioral-centered technique-A Review
Drowsiness detection using behavioral-centered technique-A Review
复制标题
使用以行为为中心的技术进行睡意检测 - 综述
DOI：
10.1109/confluence51648.2021.9377063
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Rani Astya
中科院分区：
文献类型：
--
作者：
Anjali Awasthi;P. Nand;Manish Verma;Rani Astya
驾驶员在行车过程中的疲劳状态是导致交通事故的主要原因之一，严重时会造成人员伤亡和经济损失。睡意检测有三种常规方法，即基于行为的方法、基于车辆的方法和基于生理的方法。人们已经做了大量的工作来检测睡意。不同的研究人员使用不同的机器学习算法来识别困倦。本研究的目的是比较不同研究人员使用的机器学习算法，以基于以行为为中心的技术（如眼睛，头部运动，打哈欠等）来识别困倦。
Drowsiness during driving has been seen as one of the main reason for the accidents, which results in life and economical loss. Drowsiness is detected by three conventional methods i.e. Behavior based method, vehicular based method and physiological based method. A lot of work has been done to detect the drowsiness. Different researcher uses different machine learning algorithms to identify drowsiness. The objective of this research is to compare the machine learning algorithms used by different researchers to identify the drowsiness based on behavior centered techniques like eyes, movement of head, yawning etc. for drowsiness detection.