In this paper, we explore different deep learning based approaches to detect driver fatigue. Drowsy driving results in approximately 72,000 crashes and 44,000 injuries every year in the US and detecting drowsiness and alerting the driver can save many lives. There have been many approaches to detect fatigue, of which eye closedness detection is one. We propose a framework to detect eye closedness in a captured camera frame as a gateway for detecting drowsiness. We explore two different datasets to detect eye closedness. We develop an eye model by using new Eye-blink dataset and a face model by using the Closed Eyes in the Wild (CEW). We also explore different techniques to make the models more robust by adding noise. We achieve 95.84% accuracy on our eye model and 80.01% accuracy on our face model. We also see that we can improve our accuracy on the face model by 6% via adversarial training and data augmentation. We hope that our work will be useful to the field of driver fatigue detection to avoid potential vehicle accidents related to drowsy driving.
School of Automation, Southeast University（东南大学自动化学院）
Researches show that fatigue driving is one of the important causes of road traffic accidents, so it is of great significance to study the driver fatigue recognition algorithm to improve road traffic safety. In recent years, with the development of deep learning, the field of pattern recognition has made great development. This paper designs a real-time fatigue state recognition algorithm based on spatio-temporal feature sequence, which can be mainly applied to the scene of fatigue driving recognition. The algorithm is divided into three task networks: face detection network, facial landmark d…
Indian Institute of Technology (ISM) Dhanbad（印度理工学院（ISM）丹巴德分校）
In recent years, road accidents have increased significantly. One of the major reasons for these accidents, as reported is driver fatigue. Due to continuous and longtime driving, the driver gets exhausted and drowsy which may lead to an accident. Therefore, there is a need for a system to measure the fatigue level of driver and alert him when he/she feels drowsy to avoid accidents. Thus, we propose a system which comprises of a camera installed on the car dashboard. The camera detect the driver's face and observe the alteration in its facial features and uses these features to observe the fati…
In this work various methods and algorithms for face and eyes detection are examined in order to decide which of them are applicable for use in a driver fatigue monitoring system. In the case of face detection the standard Viola-Jones face detector has shown best results, while the method of finding the eye centers by means of gradients has proven to be most appropriate in the case of eyes detection. The later method has also a potential for retrieving behavioral parameters needed for estimation of the level of driver fatigue. This possibility will be examined in future work.
LiteFat: Lightweight Spatio-Temporal Graph Learning for Real-Time Driver Fatigue Detection
School of Computing Technologies, RMIT University（计算技术学院，皇家墨尔本理工大学）；CSIRO’s Data61（CSIRO的数据61）；School of Computer Science, University of Auckland（计算机科学学院，奥克兰大学）；STEM, University of South Australia（STEM，南澳大利亚大学）
Driver fatigue is one of the important factors that cause traffic accidents, and the ever-increasing number due to diminished drivers vigilance level has become a problem of serious concern to society. Drivers with a diminished vigilance level suffer from a marked decline in their abilities of perception, recognition, and vehicle control, and therefore pose serious danger to their own life and the lives of other people. Exhaustion resulting from sleep deprivation or sleep disorders is an important factor in the creasing number of accidents. In this projected work, we discuss the various method…
Adversarial Robustness and Feature Impact Analysis for Driver Drowsiness Detection
Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD), School of Engineering, Polytechnic of Porto (ISEP/IPP)（波尔图理工学院工程学院智能工程与先进创新发展计算研究组（GECAD））；CardioID Technologies（CardioID科技公司）
Tongji University（同济大学）；Key Laboratory of Embedded System and Service Computing, Ministry of Education（嵌入式系统与服务计算教育部重点实验室）
Drowsiness driving is a major cause of traffic accidents and thus numerous previous researches have focused on driver drowsiness detection. Many drive relevant factors have been taken into consideration for fatigue detection and can lead to high precision, but there are still several serious constraints, such as most existing models are environmentally susceptible. In this paper, fatigue detection is considered as temporal action detection problem instead of image classification. The proposed detection system can be divided into four parts: (1) Localize the key patches of the detected driver p…
YawDD+: Frame-level Annotations for Accurate Yawn Prediction
Embedded Systems Division, Silicon Austria Labs（Silicon Austria Labs嵌入式系统部门）；Institute of Visual Computing, Graz University of Technology（格拉茨技术大学视觉计算研究所）；Department of Computer Science, University of Innsbruck（因斯布鲁克大学计算机科学系）
Xperi Corporation（Xperi公司）；National University of Ireland Galway（爱尔兰国立高威大学）；Prophesee（Prophesee公司）
Event cameras contain emerging, neuromorphic vision sensors that capture local light intensity changes at each pixel, generating a stream of asynchronous events. This way of acquiring visual information constitutes a departure from traditional frame based cameras and offers several significant advantages: low energy consumption, high temporal resolution, high dynamic range and low latency. Driver monitoring systems (DMS) are in-cabin safety systems designed to sense and understand a drivers physical and cognitive state. Event cameras are particularly suited to DMS due to their inherent advanta…
Countless traffic accidents often occur because of the inattention of the drivers. Many factors can contribute to distractions while driving, since objects or events to physiological conditions, as drowsiness and fatigue, do not allow the driver to stay attentive. The technological progress allowed the development and application of many solutions to detect the attention in real situations, promoting the interest of the scientific community in these last years. Commonly, these solutions identify the lack of attention and alert the driver, in order to help her/him to recover the attention, avoi…
School of Technologies, Cardiff Metropolitan University（卡迪夫城市大学技术学院）；Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology（维尔科技兰加拉詹博士萨贡塔拉科学与技术研发学院）
Mercedes-Benz AG（梅赛德斯-奔驰股份公司）；University of Tübingen（图宾根大学）
Modern advanced driver-assistance systems analyze the driving performance to gather information about the driver's state. Such systems are able, for example, to detect signs of drowsiness by evaluating the steering or lane keeping behavior and to alert the driver when the drowsiness state reaches a critical level. However, these kinds of systems have no access to direct cues about the driver's state. Hence, the aim of this work is to extend the driver drowsiness detection in vehicles using signals of a driver monitoring camera. For this purpose, 35 features related to the driver's eye blinking…
The alertness level of drivers can be estimated with the use of computer vision based methods. The level of fatigue can be found from the value of PERCLOS. It is the ratio of closed eye frames to the total frames processed. The main objective of the thesis is the design and implementation of real-time algorithms for measurement of PERCLOS. In this work we have developed a real-time system which is able to process the video onboard and to alarm the driver in case the driver is in alert. For accurate estimation of PERCLOS the frame rate should be greater than 4 and accuracy should be greater tha…
Vignan Institute of Technology and Sciences（维格纳科技学院）；VITS（维格纳科技学院）
Driving support systems, such as car navigation systems are becoming common and they support driver in several aspects. Non-intrusive method of detecting Fatigue and drowsiness based on eye-blink count and eye directed instruction controlhelps the driver to prevent from collision caused by drowsy driving. Eye detection and tracking under various conditions such as illumination, background, face alignment and facial expression makes the problem complex.Neural Network based algorithm is proposed in this paper to detect the eyes efficiently. In the proposed algorithm, first the neural Network is …
Computer Vision is considered to be one of the most important areas in research and has focused on developing many applications that has proved to be useful for both research and societal benefits. Today we have been witnessing many of the road mishaps happening just because of the lack of concentration while driving.As a part of avoiding this kind of disaster happening in day to day life there are many technologies focusing on keeping track of the vehicle drivers concentration.One such technology uses the method of eye blink detection to find out the concentration level of the driver.With the…
Awareness detection technologies have been gaining traction in a variety of enterprises; most often used for driver fatigue detection, recent research has shifted towards using computer vision technologies to analyze user attention in environments such as online classrooms. This paper aims to extend previous research on distraction detection by analyzing which visual features contribute most to predicting awareness and fatigue. We utilized the open source facial analysis toolkit OpenFace in order to analyze visual data of subjects at varying levels of attentiveness. Then, using a Support-Vecto…
Massachusetts Institute of Technology (MIT)（麻省理工学院）；Chalmers University of Technology（查尔姆斯理工大学）；SAFER
Accurate, robust, inexpensive gaze tracking in the car can help keep a driver safe by facilitating the more effective study of how to improve (1) vehicle interfaces and (2) the design of future Advanced Driver Assistance Systems. In this paper, we estimate head pose and eye pose from monocular video using methods developed extensively in prior work and ask two new interesting questions. First, how much better can we classify driver gaze using head and eye pose versus just using head pose? Second, are there individual-specific gaze strategies that strongly correlate with how much gaze classific…
The University of Texas at Dallas（德克萨斯大学达拉斯分校）；Oak Ridge National Laboratory（橡树岭国家实验室）；University of Maryland（马里兰大学）
Face de-identification algorithms have been developed in response to the prevalent use of public video recordings and surveillance cameras. Here, we evaluated the success of identity masking in the context of monitoring drivers as they actively operate a motor vehicle. We studied the effectiveness of eight de-identification algorithms using human perceivers and a state-of-the-art deep convolutional neural network (CNN). We used a standard face recognition experiment in which human subjects studied high-resolution (studio-style) images to learn driver identities. Subjects were tested subsequent…
Institute for Anthropomatics and Robotics（拟人学与机器人学研究所）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
Visual recognition inside the vehicle cabin leads to safer driving and more intuitive human-vehicle interaction but such systems face substantial obstacles as they need to capture different granularities of driver behaviour while dealing with highly limited body visibility and changing illumination. Multimodal recognition mitigates a number of such issues: prediction outcomes of different sensors complement each other due to different modality-specific strengths and weaknesses. While several late fusion methods have been considered in previously published frameworks, they constantly feature di…
