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

Alberto Fernández Villán

摘要

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.

机译：驾驶是一项需要进行驾驶的人高度集中的活动，因为稍有疏忽就足以引起事故，并造成物质和/或人员损失。根据世界卫生组织（WHO）在2013年发布的最新研究，估计有125万人死于交通事故，而有20至5000万人没有死亡，但后果是慢性病。这些事故中有许多是由于疏忽引起的。这个术语包含了诸如分散注意力和嗜睡之类的不同条件，正是这些条件导致更多的死亡。许多出版物和研究都试图设定数字来指示注意力不集中（及其亚型）的后果，但是由于所有这些研究都是在不同的地方，不同的时间范围内进行的，因此没有确切的数字说明由于注意力不集中造成的事故。，在不同条件下。总体而言，据估计，注意力不集中会导致25％至75％的事故和接近事故。在十个欧洲国家进行的嗜睡驾驶研究表明，疲劳有可能使反应时间增加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主要原因。根据交通总局（DGT）的统计，分心是致命事故中的首次违规，占案件的13.15％。总体而言，考虑到分散注意力和嗜睡，西班牙驾驶员不专心驾驶的最新统计数字令人震惊，似乎是导致死亡的主要原因（36％），远远超过了超速（21％）或饮酒（11％）。该博士论文的原因是上述数字的直接后果，其目的是提供一种机制，以帮助减少使用计算机视觉技术的驾驶员注意力不集中的影响。面部属性的提取可用于稳健地检测注意力不集中。具体来说，研究建立了表征驾驶员注意力分散的参考框架，以便为将来的研究提供坚实的基础[1]。基于这项研究[1]，提出了一种基于视觉特征分析的体系结构，该体系结构通过使用计算机视觉和自动学习技术来检测注意力分散和嗜睡[2]，并结合了多种创新要素，从而进行了构建和验证。为了以完全自主的方式进行操作，以可靠地检测表征驾驶员分心和嗜睡的主要视觉指示器：（1）回顾了计算机视觉技术在监测分心的监控系统开发中的作用[3] ; （2）基于局部二值模式（LBP）和支持向量机（SVM）的人脸处理算法，用于检测人脸属性[4]; （3）使用标记和机器学习算法[2]的驾驶员是否存在的检测单元; （4）基于相机位置和人脸检测算法的鲁棒人脸跟踪算法[2]; （5）改进人脸对齐和归一化算法的眼睛状态检测[3]; （6）基于随时间变化的眼睛状态检测的驾驶员睡意检测[2]; （7）基于头部随时间的位置进行驾驶员注意力分散检测[2]。首先，通过参考数据库测试组成该架构的不同模块，对该架构进行了验证;其次，与实际环境中的用户一起，在两种情况下均获得了出色的结果，并为车辆环境中的嵌入式设备提供了适当的计算负载[2] ]。与在实际环境中进行的测试相关，有16位驾驶员参与了多项活动，模仿了困倦和分心的不同迹象。总体而言，考虑到所有活动和所有驱动因素，其准确性为93.11％[2]。此外，本论文的其他贡献已经在受控环境中进行了实验验证，但有望被包含在上述架构中：（1）在检测眼睛状态之前进行眼镜检测算法[3]（如果驾驶员戴着眼镜或太阳镜，则可以准确获得[1]）; （2）人脸识别和欺骗检测算法识别驾驶员[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]（使用此信息，可以获得认知负荷和压力[1]）; （4）实时大数据架构，用于处理大量相对较小的图像[7]。因此，将来的工作将包括这些要点，以完成体系结构。

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

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