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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 …
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 …
Automatic robot Manoeuvres detection using computer vision and deep learning techniques: a perspective of internet of robotics things (IoRT)
To minimize any impediments in real-time Internet of Things (IoT)-enabled robotics
applications, this study demonstrated how to build and deploy a revolutionary framework …
applications, this study demonstrated how to build and deploy a revolutionary framework …
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 …
Driver anomaly detection: A dataset and contrastive learning approach
Distracted drivers are more likely to fail to anticipate hazards, which result in car accidents.
Therefore, detecting anomalies in drivers' actions (ie, any action deviating from normal …
Therefore, detecting anomalies in drivers' actions (ie, any action deviating from normal …
[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 …
Vision-language models can identify distracted driver behavior from naturalistic videos
Recognizing the activities causing distraction in real-world driving scenarios is critical for
ensuring the safety and reliability of both drivers and pedestrians on the roadways …
ensuring the safety and reliability of both drivers and pedestrians on the roadways …
Learning accurate, speedy, lightweight CNNs via instance-specific multi-teacher knowledge distillation for distracted driver posture identification
For deployment on an embedded processor for distracted driver classification, the model
should satisfy the demand for both high accuracy, real-time inference, and limited storage …
should satisfy the demand for both high accuracy, real-time inference, and limited storage …
Deep cnn, body pose, and body-object interaction features for drivers' activity monitoring
Automatic recognition and prediction of in-vehicle human activities has a significant impact
on the next generation of driver assistance and intelligent autonomous vehicles. In this …
on the next generation of driver assistance and intelligent autonomous vehicles. In this …
Efficient driver anomaly detection via conditional temporal proposal and classification network
Detecting driver inattentive behaviors is crucial for driving safety in a driver monitoring
system (DMS). Recent works treat driver distraction detection as a multiclass action …
system (DMS). Recent works treat driver distraction detection as a multiclass action …