基于面部多特征融合的疲劳驾驶检测综述

doi:10.19678/j.issn.1000-3428.0066661

计算机工程 ›› 2023, Vol. 49 ›› Issue (11): 1-12. doi: 10.19678/j.issn.1000-3428.0066661

基于面部多特征融合的疲劳驾驶检测综述

王畅¹^,², 李雷孝¹^,²^,*, 杨艳艳¹^,²

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

收稿日期:2023-01-03 出版日期:2023-11-15 发布日期:2023-11-08
通讯作者: 李雷孝
作者简介:
王畅(1997-), 女, 硕士研究生, 主研方向为计算机视觉、云计算、大数据处理
杨艳艳, 硕士研究生
基金资助:
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); 内蒙古自治区科技成果转化专项资金项目(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)

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

Chang WANG¹^,², Leixiao LI¹^,²^,*, Yanyan YANG¹^,²

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

Received:2023-01-03 Online:2023-11-15 Published:2023-11-08
Contact: Leixiao LI

摘要/Abstract

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。

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

Abstract:

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.

Key words: fatigue driving detection, facial feature, feature extraction, feature discrimination, multi-feature fusion

王畅, 李雷孝, 杨艳艳. 基于面部多特征融合的疲劳驾驶检测综述[J]. 计算机工程, 2023, 49(11): 1-12.

Chang WANG, Leixiao LI, Yanyan YANG. Survey of Fatigue Driving Detection Based on Facial Multi-Feature Fusion[J]. Computer Engineering, 2023, 49(11): 1-12.

https://www.ecice06.com/CN/Y2023/V49/I11/1

图/表 21

图1 疲劳驾驶判别所需的面部特征

Fig.1 Facial features required for fatigue driving discrimination

图2 基于面部多特征的疲劳驾驶检测流程

Fig.2 Fatigue driving detection procedure based on facial multiple features

图3 头部姿态的变动方向

Fig.3 Change direction of head posture

图4 眼部关键点信息

Fig.4 Eye key point information

图5 正常状态下EAR值的变化情况

Fig.5 Changes of EAR values under normal conditions

图6 疲劳状态下EAR值的变化情况

Fig.6 Changes of EAR values under fatigue conditions

图7 嘴部关键点信息

Fig.7 Mouth key point information

图8 正常状态下MAR值的变化情况

Fig.8 Changes of MAR values under normal conditions

图9 疲劳状态下MAR值的变化情况

Fig.9 Changes of MAR values under fatigue conditions

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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

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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

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