基于面部多特征融合的疲劳驾驶检测综述  

Survey of Fatigue Driving Detection Based on Facial Multi-Feature Fusion

作　　者：王畅 李雷孝 杨艳艳 WANG Chang;LI Leixiao;YANG Yanyan(College of Data Science and Application,Inner Mongolia University of Technology,Hohhot 010080,China;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)

机构地区：[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

出　　处：《计算机工程》2023年第11期1-12,共12页Computer Engineering

基　　金：国家自然科学基金(62362055);内蒙古自治区科技成果转化专项资金项目(2020CG0073,2021CG0033);内蒙古自治区科技计划项目(2020GG0104);内蒙古自治区高等学校青年科技英才支持计划项目(NJYT22084);内蒙古自治区自然科学基金项目(2021MS06019);内蒙古自治区高等学校科学研究项目(NJZY21317);内蒙古工业大学科学研究重点项目(ZZ202017)。

摘　　要：基于计算机视觉的疲劳驾驶检测方法具有非侵入性等优点,不会对驾驶行为产生影响,在实际场景中便于应用。随着计算机技术的发展,越来越多的学者研究基于计算机视觉的疲劳驾驶检测方法。疲劳驾驶行为主要体现在面部和肢体上,在计算机视觉领域,面部行为较肢体行为更易获取,因此,基于面部特征的疲劳驾驶检测方法成为疲劳驾驶检测领域的重要研究方向。综合分析多种基于驾驶员面部多特征的疲劳驾驶检测方法,对国内外最新研究成果进行总结。介绍驾驶员面部不同特征在疲劳状态下的具体行为体现,阐述基于面部多特征的疲劳驾驶检测流程。根据面部不同特征对国内外的研究成果进行分类,并整理不同的特征提取方法和状态判别方法。通过不同特征在疲劳状态下产生的各种行为归纳不同方法判别驾驶员疲劳状态时使用的参数。同时,总结当前研究成果中使用面部多特征综合判别疲劳驾驶的方法,分析不同方法间的相同点和差异性。在此基础上,讨论当前基于面部多特征融合的疲劳驾驶检测领域存在的不足,并对该领域的未来研究方向进行展望。The fatigue driving detection method based on computer vision has the advantage of being noninvasive and does not affect driving behavior,making it easy to apply in practical scenarios.With the development of computer technology,an increasing number of researchers are studying fatigue driving detection methods based on computer vision.Fatigue driving behavior is mainly reflected in the face and limbs.Furthermore,in the field of computer vision,facial behavior is easier to obtain than physical behavior.Therefore,facial-feature-based fatigue driving detection methods have become an important research direction in the field of fatigue driving detection.Various fatigue driving detection methods are analyzed comprehensively based on multiple facial features of drivers,and the latest research results worldwide are summarized.The specific behaviors of drivers with different facial features under fatigue conditions are introduced,and the fatigue driving detection process is discussed based on multiple facial features.Results from research conducted worldwide are classified based on different facial features,and different feature extraction methods and state discrimination methods are classified.The parameters used to distinguish driver fatigue status are summarized based on the various behaviors generated by different features in a state of fatigue.Furthermore,current research results on the use of facial multi-feature comprehensive discrimination for fatigue driving are described,and the similarities and differences of different methods are analyzed.On this basis,the shortcomings in the current field of fatigue driving detection based on facial multi-feature fusion are discussed,and future research directions in this field are described.

关 键 词：疲劳驾驶检测 面部特征 特征提取 特征判别 多特征融合 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]