A survey on driver behavior analysis from in-vehicle cameras
Distracted or drowsy driving is unsafe driving behavior responsible for thousands of crashes
every year. Studying driver behavior has challenges associated with observing drivers in …
every year. Studying driver behavior has challenges associated with observing drivers in …
Detecting and recognizing driver distraction through various data modality using machine learning: A review, recent advances, simplified framework and open …
Driver distraction is one of the main causes of fatal traffic accidents. Therefore, the ability to
detect driver inattention is essential in building a safe yet intelligent transportation system …
detect driver inattention is essential in building a safe yet intelligent transportation system …
Driver activity recognition for intelligent vehicles: A deep learning approach
Driver decisions and behaviors are essential factors that can affect the driving safety. To
understand the driver behaviors, a driver activities recognition system is designed based on …
understand the driver behaviors, a driver activities recognition system is designed based on …
Driver distraction identification with an ensemble of convolutional neural networks
HM Eraqi, Y Abouelnaga, MH Saad… - Journal of advanced …, 2019 - Wiley Online Library
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 …
accidents worldwide and the number has been continuously increasing over the last few …
Drive&act: A multi-modal dataset for fine-grained driver behavior recognition in autonomous vehicles
We introduce the novel domain-specific Drive&Act benchmark for fine-grained
categorization of driver behavior. Our dataset features twelve hours and over 9.6 million …
categorization of driver behavior. Our dataset features twelve hours and over 9.6 million …
The evolution of bashlite and mirai iot botnets
A Marzano, D Alexander, O Fonseca… - … IEEE Symposium on …, 2018 - ieeexplore.ieee.org
Vulnerable IoT devices are powerful platforms for building botnets that cause billion-dollar
losses every year. In this work, we study Bashlite botnets and their successors, Mirai botnets …
losses every year. In this work, we study Bashlite botnets and their successors, Mirai botnets …
Driver anomaly quantification for intelligent vehicles: A contrastive learning approach with representation clustering
Driver anomaly quantification is a fundamental capability to support human-centric driving
systems of intelligent vehicles. Existing studies usually treat it as a classification task and …
systems of intelligent vehicles. Existing studies usually treat it as a classification task and …
Detection of distracted driver using convolutional neural network
Abstract Number of road accidents is continuously increasing in last few years worldwide. As
per the survey of National Highway Traffic Safety Administrator, nearly one in five motor …
per the survey of National Highway Traffic Safety Administrator, nearly one in five motor …
Dmd: A large-scale multi-modal driver monitoring dataset for attention and alertness analysis
Vision is the richest and most cost-effective technology for Driver Monitoring Systems (DMS),
especially after the recent success of Deep Learning (DL) methods. The lack of sufficiently …
especially after the recent success of Deep Learning (DL) methods. The lack of sufficiently …
Real‐time detection of distracted driving based on deep learning
Driver distraction is a leading factor in car crashes. With a goal to reduce traffic accidents
and improve transportation safety, this study proposes a driver distraction detection system …
and improve transportation safety, this study proposes a driver distraction detection system …