计算机科学与探索 ›› 2023, Vol. 17 ›› Issue (6): 1249-1267.DOI: 10.3778/j.issn.1673-9418.2208041

• 前沿·综述 • 上一篇    下一篇

提取驾驶员面部特征的疲劳驾驶检测研究综述

杨艳艳,李雷孝,林浩   

  1. 1. 内蒙古工业大学 数据科学与应用学院,呼和浩特 010080
    2. 内蒙古自治区科学技术厅 内蒙古自治区基于大数据的软件服务工程技术研究中心,呼和浩特 010080
    3. 天津理工大学 计算机科学与工程学院,天津 300384
  • 出版日期:2023-06-01 发布日期:2023-06-01

Review of Research on Fatigue Driving Detection Based on Driver Facial Features

YANG Yanyan, LI Leixiao, LIN Hao   

  1. 1. College of Data Science and Application, Inner Mongolia University of Technology, Hohhot 010080, China
    2. Inner Mongolia Autonomous Region Software Service Engineering Technology Research Center Based on Big Data, Science and Technology Department of Inner Mongolia Autonomous Region, Hohhot 010080, China
    3. College of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
  • Online:2023-06-01 Published:2023-06-01

摘要: 疲劳驾驶是威胁驾驶员人身安全以及道路交通安全的主要因素之一。高效精准的疲劳驾驶检测方法可以有效地保障驾驶员及其周围交通安全,维护交通秩序,减少财产损失和人员伤亡。由于基于驾驶员生理特征和基于车辆行驶信息的疲劳驾驶检测方法具有对驾驶员不友好、影响因素较多等局限性,使得基于驾驶员面部特征的疲劳驾驶检测方法成为研究热点。首先简述了疲劳驾驶面部特征表现,总结了疲劳驾驶领域常用公开数据集的优缺点和应用场景;其次使用公开数据集,通过对比实验,分析研究了疲劳驾驶检测领域常用人脸检测算法的优势和不足;随后给出了基于驾驶员面部特征的疲劳驾驶检测方法流程,总结分析了流程中关键步骤所使用的方法和技术;另外归纳整理了疲劳驾驶领域常用的疲劳判别参数和疲劳驾驶结果预测方法;最后对全文进行总结,给出了基于驾驶员面部特征的疲劳驾驶检测方法目前所面临的挑战,并对未来研究进行了展望。

关键词: 驾驶员面部特征, 疲劳驾驶检测, 疲劳判别参数, 特征提取, 人脸检测

Abstract: Fatigue driving is one of the main factors that threaten the safety of drivers and traffic. Efficient and accurate fatigue driving detection method can effectively ensure the safety of drivers and their surrounding traffic, maintain traffic order, and reduce property losses and casualties. The fatigue driving detection method based on the driver’s physiological characteristics and vehicle driving information has many limitations, such as being unfriendly to the driver and having numerous influencing factors. Therefore, the fatigue driving detection method based on the driver’s facial features has become a research hotspot. Firstly, the facial feature performance of fatigue driving is described, and advantages, disadvantages and application scenarios of common public datasets in the field of fatigue driving are summarized. Secondly, the advantages and disadvantages of common face detection algorithms in the field of fatigue driving detection are analyzed and studied by using open datasets and comparative experiments. Then, this paper generalizes the process of detection methods based on driver facial features, whose methods and technologies used in the key steps are reviewed. Furthermore, fatigue discriminant parameters and methods of fatigue driving results prediction are summarized. Finally, this paper ends with a discussion of current challenges of fatigue driving detection methods based on driver facial features and looks forward to the future research.

Key words: driver facial features, fatigue driving test, fatigue discriminant parameters, feature extraction, face detection