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A survey on vision-based driver distraction analysis
Motor vehicle crashes are great threats to our life, which may result in numerous fatalities, as
well as tremendous economic and societal costs. Driver inattention, either distraction or …
well as tremendous economic and societal costs. Driver inattention, either distraction or …
[HTML][HTML] Real-time driver distraction recognition: A hybrid genetic deep network based approach
Distracting while driving is a serious issue that causes serious direct and indirect harm to the
society. To avoid these problems, detecting dangerous drivers' behaviour is very important …
society. To avoid these problems, detecting dangerous drivers' behaviour is very important …
E2DR: A deep learning ensemble-based driver distraction detection with recommendations model
The increasing number of car accidents is a significant issue in current transportation
systems. According to the World Health Organization (WHO), road accidents are the eighth …
systems. According to the World Health Organization (WHO), road accidents are the eighth …
A new unsupervised deep learning algorithm for fine-grained detection of driver distraction
Traffic accidents caused by distracted drivers account for a large proportion of traffic
accidents each year, and monitoring the driving state of drivers to avoid traffic accidents …
accidents each year, and monitoring the driving state of drivers to avoid traffic accidents …
Driver distraction detection based on the true driver's focus of attention
Effective driver distraction detection (DDD) can significantly improve driving safety. Inspired
by the definition of driver distraction, this work aims to detect driver distraction based on the …
by the definition of driver distraction, this work aims to detect driver distraction based on the …
[HTML][HTML] Deep learning approach based on residual neural network and SVM classifier for driver's distraction detection
In the last decade, distraction detection of a driver gained a lot of significance due to
increases in the number of accidents. Many solutions, such as feature based, statistical …
increases in the number of accidents. Many solutions, such as feature based, statistical …
Learning driver-irrelevant features for generalizable driver behavior recognition
Traffic accidents caused by driver distractions have seriously endangered public safety, with
driver distractions typically stemming from behaviors beyond safe driving. Recently, vision …
driver distractions typically stemming from behaviors beyond safe driving. Recently, vision …
Multi-stream deep convolution neural network with ensemble learning for facial micro-expression recognition
Micro-expression recognition has gained much attention in research communities. Among
its proposed solutions, deep learning approaches have shown promising results over the …
its proposed solutions, deep learning approaches have shown promising results over the …
Improving real-time driver distraction detection via constrained attention mechanism
H Gao, Y Liu - Engineering Applications of Artificial Intelligence, 2024 - Elsevier
Real-time driving distraction detection has garnered significant attention due to its potential
to build various driving safety protections such as distraction warnings and driver assistance …
to build various driving safety protections such as distraction warnings and driver assistance …
Driver drowsiness detection with region-of-interest selection based spatio-temporal deep convolutional-lstm
Driver fatigue and drowsiness instigate road traffic accidents while driving throughout the
years. to reduce road traffic injuries and fatality cases, a real-time drowsiness detection …
years. to reduce road traffic injuries and fatality cases, a real-time drowsiness detection …