The use of mobiles phones when driving have been a major factor when it comes to road traffic incidents and the process of capturing such violations can be a laborious task. Advancements in both modern object detection frameworks and high-performance hardware has paved the way for a more automated approach when it comes to video surveillance. In this work, we propose a custom-trained state-of-the-art object detector to work with roadside cameras to capture driver phone usage without the need for human intervention. The proposed approach also addresses the issues caused by windscreen glare and introduces the steps required to remedy this. Twelve pre-trained models are fine-tuned with our custom dataset using four popular object detection methods: YOLO, SSD, Faster R-CNN, and CenterNet. Out of all the object detectors tested, the YOLO yields the highest accuracy levels of up to 96% (AP10) and frame rates of up to ~30 FPS. DeepSort object tracking algorithm is also integrated into the best-performing model to collect records of only the unique violations, and enable the proposed approach to count the number of vehicles. The proposed automated system will collect the output images of the identified violations, timestamps of each violation, and total vehicle count. Data can be accessed via a purpose-built user interface.
University of California, San Diego（加利福尼亚大学圣地亚哥分校）；Laboratory for Intelligent and Safe Automobiles（智能与安全汽车实验室）
The intelligent vehicle community has devoted considerable efforts to model driver behavior, and in particular to detect and overcome driver distraction in an effort to reduce accidents caused by driver negligence. However, as the domain increasingly shifts towards autonomous and semi-autonomous solutions, the driver is no longer integral to the decision making process, indicating a need to refocus efforts elsewhere. To this end, we propose to study pedestrian distraction instead. In particular, we focus on detecting pedestrians who are engaged in secondary activities involving their cellphone…
This study proposes an approach to segment human object from a depth image based on histogram of depth values. The region of interest is first extracted based on a predefined threshold for histogram regions. A region growing process is then employed to separate multiple human bodies with the same depth interval. Our contribution is the identification of an adaptive growth threshold based on the detected histogram region. To demonstrate the effectiveness of the proposed method, an application in driver distraction detection was introduced. After successfully extracting the driver's position ins…
Naturalistic driving data (NDD) is an important source of information to understand crash causation and human factors and to further develop crash avoidance countermeasures. Videos recorded while driving are often included in such datasets. While there is often a large amount of video data in NDD, only a small portion of them can be annotated by human coders and used for research, which underuses all video data. In this paper, we explored a computer vision method to automatically extract the information we need from videos. More specifically, we developed a 3D ConvNet algorithm to automaticall…
Santa Catarina State University (UDESC)（圣卡塔琳娜州立大学（UDESC））；University of Campinas (Unicamp)（坎皮纳斯大学（Unicamp））
It is estimated that 80% of crashes and 65% of near collisions involved drivers inattentive to traffic for three seconds before the event. This paper develops an algorithm for extracting characteristics allowing the cell phones identification used during driving a vehicle. Experiments were performed on sets of images with 100 positive images (with phone) and the other 100 negative images (no phone), containing frontal images of the driver. Support Vector Machine (SVM) with Polynomial kernel is the most advantageous classification system to the features provided by the algorithm, obtaining a su…
Wearing a seatbelt appropriately while driving can reduce serious crash-related injuries or deaths by about half. However, current seatbelt reminder system has multiple shortcomings, such as can be easily fooled by a "Seatbelt Warning Stopper", and cannot recognize incorrect usages for example seating in front of a buckled seatbelt or wearing a seatbelt under the arm. General seatbelt usage recognition has many challenges, to name a few, lacking of color information in Infrared (IR) cameras, strong distortion caused by wide Field of View (FoV) fisheye lens, low contrast between belt and its ba…
Extracting hand regions and their grasp information from images robustly in real-time is critical for occupants' safety and in-vehicular infotainment applications. It must however, be noted that naturalistic driving scenes suffer from rapidly changing illumination and occlusion. This is aggravated by the fact that hands are highly deformable objects, and change in appearance frequently. This work addresses the task of accurately localizing driver hands and classifying the grasp state of each hand. We use a fast ConvNet to first detect likely hand regions. Next, a pixel-based skin classifier th…
Air University（空军大学）；Vellore Institute of Technology（韦洛尔理工学院）；Comenius University in Bratislava（布拉迪斯拉发夸美纽斯大学）
Cash payment is still king in several markets, accounting for more than 90\ of the payments in almost all the developing countries. The usage of mobile phones is pretty ordinary in this present era. Mobile phones have become an inseparable friend for many users, serving much more than just communication tools. Every subsequent person is heavily relying on them due to multifaceted usage and affordability. Every person wants to manage his/her daily transactions and related issues by using his/her mobile phone. With the rise and advancements of mobile-specific security, threats are evolving as we…
Driver drowsiness increases crash risk, leading to substantial road trauma each year. Drowsiness detection methods have received considerable attention, but few studies have investigated the implementation of a detection approach on a mobile phone. Phone applications reduce the need for specialised hardware and hence, enable a cost-effective roll-out of the technology across the driving population. While it has been shown that three-dimensional (3D) operations are more suitable for spatiotemporal feature learning, current methods for drowsiness detection commonly use frame-based, multi-step ap…
University of Arizona（亚利桑那大学）；Cornell University（康奈尔大学）
To help prevent motor vehicle accidents, there has been significant interest in finding an automated method to recognize signs of driver distraction, such as talking to passengers, fixing hair and makeup, eating and drinking, and using a mobile phone. In this paper, we present an automated supervised learning method called Drive-Net for driver distraction detection. Drive-Net uses a combination of a convolutional neural network (CNN) and a random decision forest for classifying images of a driver. We compare the performance of our proposed Drive-Net to two other popular machine-learning approa…
The American University in Cairo（开罗美国大学）；Technical University of Munich（慕尼黑工业大学）；Ain Shams University（艾因夏姆斯大学）
The World Health Organization (WHO) reported 1.25 million deaths yearly due to road traffic accidents worldwide and the number has been continuously increasing over the last few years. Nearly fifth of these accidents are caused by distracted drivers. Existing work of distracted driver detection is concerned with a small set of distractions (mostly, cell phone usage). Unreliable ad-hoc methods are often used.In this paper, we present the first publicly available dataset for driver distraction identification with more distraction postures than existing alternatives. In addition, we propose a rel…
Ohio State University（俄亥俄州立大学）；Lyft Inc.（Lyft公司）；Google Inc.（谷歌公司）；Nationwide Mutual Insurance Co.（全美互惠保险公司）
Identifying driving styles is the task of analyzing the behavior of drivers in order to capture variations that will serve to discriminate different drivers from each other. This task has become a prerequisite for a variety of applications, including usage-based insurance, driver coaching, driver action prediction, and even in designing autonomous vehicles; because driving style encodes essential information needed by these applications. In this paper, we present a deep-neural-network architecture, we term D-CRNN, for building high-fidelity representations for driving style, that combine the p…
Great Learning/Northwestern University（Great Learning/西北大学）；Great Learning
With over 50 million car sales annually and over 1.3 million deaths every year due to motor accidents we have chosen this space. India accounts for 11 per cent of global death in road accidents. Drivers are held responsible for 78% of accidents. Road safety problems in developing countries is a major concern and human behavior is ascribed as one of the main causes and accelerators of road safety problems. Driver distraction has been identified as the main reason for accidents. Distractions can be caused due to reasons such as mobile usage, drinking, operating instruments, facial makeup, social…
DAOS: A Multimodal In-cabin Behavior Monitoring with Driver Action-Object Synergy Dataset
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore（南洋理工大学电子与电气工程学院）；College of Computer Science and Technology, Harbin Engineering University（哈尔滨工程大学计算机科学与技术学院）；Singapore University of Technology and Design (SUTD), Singapore（新加坡科技设计大学）；Continental Automotive Singapore Pte. Ltd.
