Facial attributes recognition using computer vision to detect drowsiness and distraction in drivers

Q4 Computer Science
Alberto Fernández Villán
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Many publications and research have tried to set figures indicating the consequences of inattention (and its subtypes), but there is no exact number of the accidents caused by inattention since all these studies have been carried out in different places, different time frames and, therefore, under different conditions. Overall, it has been estimated that inattention causes between 25% and 75% of accidents and near-accidents. A study on drowsiness while driving in ten European countries found that fatigue risks increasing reaction time by 86% and it is the fourth leading cause of death on Spanish roads. Distraction is also a major contributor to fatal accidents in Spain. According to the Directorate General of Traffic (DGT), distraction is the first violation found in fatal accidents, 13.15% of the cases. Overall, considering both distraction and drowsiness, the latest statistics on inattentive driving in Spanish drivers are alarming, appearing as the leading cause of fatalities (36%), well above excessive speed (21%) or alcohol consumption (11%). The reason for this PhD thesis is the direct consequences of the abovementioned figures and its purpose is to provide mechanisms to help reduce driver inattention effects using computer vision techniques. The extraction of facial attributes can be used to detect inattention robustly. Specifically, research establishes a frame of reference to characterize distraction in drivers in order to provide solid foundations for future research [1]. Based on this research [1], an architecture based on the analysis of visual characteristics has been proposed, constructed and validated by using techniques of computer vision and automatic learning for the detection of both distraction and drowsiness [2], integrating several innovative elements in order to operate in a completely autonomous way for the robust detection of the main visual indicators characterizing the driver’s both distraction and drowsiness: (1) a review of the role of computer vision technology applied to the development of monitoring systems to detect distraction [3]; (2) a face processing algorithm based on Local Binary Patterns (LBP) and Support Vector Machine (SVM) to detect facial attributes [4]; (3) detection unit for the presence/absence of the driver using both a marker and a machine learning algorithm [2]; (4) robust face tracking algorithm based on both the position of the camera and the face detection algorithm [2]; (5) a face alignment and normalization algorithm to improve the eyes state detection [3]; (6) driver drowsiness detection based on the eyes state detection over time [2]; (7) driver distraction detection based on the position of the head over time [2]. 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引用次数: 3

Abstract

Driving is an activity that requires a high degree of concentration on the part of the person who performs it, since the slightest negligence is sufficient to provoke an accident with the consequent material and/or human losses. According to the most recent study published by the World Health Organization (WHO) in 2013, it was estimated that 1.25 million people died as a result of traffic accidents, whereas between 20 and 50 million did not die but consequences resulted in chronic conditions. Many of these accidents are caused by what is known as inattention. This term encloses different conditions such as distraction and drowsiness, which are, precisely, the ones that cause more fatalities. Many publications and research have tried to set figures indicating the consequences of inattention (and its subtypes), but there is no exact number of the accidents caused by inattention since all these studies have been carried out in different places, different time frames and, therefore, under different conditions. Overall, it has been estimated that inattention causes between 25% and 75% of accidents and near-accidents. A study on drowsiness while driving in ten European countries found that fatigue risks increasing reaction time by 86% and it is the fourth leading cause of death on Spanish roads. Distraction is also a major contributor to fatal accidents in Spain. According to the Directorate General of Traffic (DGT), distraction is the first violation found in fatal accidents, 13.15% of the cases. Overall, considering both distraction and drowsiness, the latest statistics on inattentive driving in Spanish drivers are alarming, appearing as the leading cause of fatalities (36%), well above excessive speed (21%) or alcohol consumption (11%). The reason for this PhD thesis is the direct consequences of the abovementioned figures and its purpose is to provide mechanisms to help reduce driver inattention effects using computer vision techniques. The extraction of facial attributes can be used to detect inattention robustly. Specifically, research establishes a frame of reference to characterize distraction in drivers in order to provide solid foundations for future research [1]. Based on this research [1], an architecture based on the analysis of visual characteristics has been proposed, constructed and validated by using techniques of computer vision and automatic learning for the detection of both distraction and drowsiness [2], integrating several innovative elements in order to operate in a completely autonomous way for the robust detection of the main visual indicators characterizing the driver’s both distraction and drowsiness: (1) a review of the role of computer vision technology applied to the development of monitoring systems to detect distraction [3]; (2) a face processing algorithm based on Local Binary Patterns (LBP) and Support Vector Machine (SVM) to detect facial attributes [4]; (3) detection unit for the presence/absence of the driver using both a marker and a machine learning algorithm [2]; (4) robust face tracking algorithm based on both the position of the camera and the face detection algorithm [2]; (5) a face alignment and normalization algorithm to improve the eyes state detection [3]; (6) driver drowsiness detection based on the eyes state detection over time [2]; (7) driver distraction detection based on the position of the head over time [2]. This architecture has been validated, firstly, with reference databases testing the different modules that compose it, and, secondly, with users in real environments, obtaining in both cases, excellent results with a suitable computational load for the embedded devices in vehicle environments [2]. In connection with the tests performed in real-world settings, 16 drivers were involved performing several activities imitating different signs of sleepiness and distraction. Overall, an accuracy of 93.11% is obtained considering all activities and all drivers [2]. Additionally, other contributions of this thesis have been experimentally validated in controlled settings, but are expected to be included in the abovementioned architecture: (1) glasses detection algorithm prior to the detection of the eyes state [3] (the eyes state can not be accurately obtained if the driver is wearing glasses or sunglasses [1]); (2) face recognition and spoofing detection algorithm to identify the driver [5]; (3) physiological information (Heart Rate, Respiration Rate and Heart Rate Variability) are extracted from the users face [6] (using this information, cognitive load and stress can be obtained [1]); (4) a real-time big data architecture to process a large number of relatively small-sized images [7]. Therefore, future work will include these points to complete the architecture.
使用计算机视觉进行面部特征识别,以检测驾驶员的困倦和分心
驾驶是一项需要高度集中注意力的活动,因为最轻微的疏忽就足以引发事故,造成物质和/或人身损失。根据世界卫生组织(世卫组织)2013年发表的最新研究报告,估计有125万人死于交通事故,而有2000万至5000万人虽然没有死亡,但其后果导致了慢性病。这些事故中有许多是由于注意力不集中造成的。这个术语包括分心和困倦等不同的情况,这些情况恰恰是导致更多死亡的原因。许多出版物和研究都试图设定数字,表明注意力不集中(及其亚型)的后果,但由于所有这些研究都是在不同的地点、不同的时间框架、因此在不同的条件下进行的,因此没有由注意力不集中引起的事故的确切数字。总的来说,据估计,25%到75%的事故和意外是由于注意力不集中造成的。一项在十个欧洲国家进行的关于开车时困倦的研究发现,疲劳可能会使反应时间增加86%,这是西班牙道路上第四大死亡原因。分心也是西班牙致命事故的一个主要原因。根据交通总局(DGT)的数据,在致命事故中,注意力分散是第一个违规行为,占13.15%。总的来说,考虑到分心和嗜睡,西班牙司机的最新统计数据令人震惊,成为导致死亡的主要原因(36%),远高于超速(21%)或饮酒(11%)。这篇博士论文的原因是上述数字的直接后果,其目的是提供利用计算机视觉技术帮助减少驾驶员注意力不集中效应的机制。人脸属性的提取可以用来鲁棒地检测注意力不集中。具体而言,研究建立了一个表征驾驶员分心的参考框架,为未来的研究提供坚实的基础[1]。在此研究基础上[1],提出了一种基于视觉特征分析的架构,利用计算机视觉和自动学习技术构建并验证了用于分心和困倦检测的架构[2],整合了几个创新元素,以便以完全自主的方式运行,以鲁棒检测表征驾驶员分心和困倦的主要视觉指标:(1)回顾了计算机视觉技术在监测系统开发中用于检测分心的作用[3];(2)基于局部二值模式(LBP)和支持向量机(SVM)的人脸属性检测算法[4];(3)使用标记和机器学习算法[2]来检测驾驶员是否存在;(4)基于摄像机位置和人脸检测算法的鲁棒人脸跟踪算法[2];(5)改进眼睛状态检测的人脸对齐和归一化算法[3];(6)基于眼睛状态随时间变化的驾驶员睡意检测[2];(7)基于头部位置随时间变化的驾驶员分心检测[2]。该架构首先通过参考数据库测试了构成该架构的不同模块,其次通过真实环境中的用户进行了验证,在这两种情况下都获得了出色的结果,并为车载环境中的嵌入式设备提供了合适的计算负载[2]。与在现实环境中进行的测试相联系,16名司机参与了几项活动,模仿不同的困倦和分心迹象。总体而言,考虑所有活动和所有驱动因素,准确率为93.11%[2]。此外,本文的其他贡献已经在受控环境下进行了实验验证,但预计将包含在上述架构中:(1)在检测眼睛状态之前进行眼镜检测算法[3](如果驾驶员戴眼镜或太阳镜则无法准确获取眼睛状态[1]);(2)人脸识别和欺骗检测算法,用于识别驾驶员[5];(3)从用户面部提取生理信息(心率、呼吸率和心率变异性)[6](利用这些信息可以获得认知负荷和压力[1]);(4)实时大数据架构,处理大量相对小尺寸的图像[7]。因此,未来的工作将包括这些点来完成架构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Electronic Letters on Computer Vision and Image Analysis
Electronic Letters on Computer Vision and Image Analysis Computer Science-Computer Vision and Pattern Recognition
CiteScore
2.50
自引率
0.00%
发文量
19
审稿时长
12 weeks
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