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 …
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 …
Toward human activity recognition: a survey
Human activity recognition (HAR) is a complex and multifaceted problem. The research
community has reported numerous approaches to perform HAR. Along with HAR …
community has reported numerous approaches to perform HAR. Along with HAR …
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 …
100-driver: a large-scale, diverse dataset for distracted driver classification
Distracted driver classification (DDC) plays an important role in ensuring driving safety.
Although many datasets are introduced to support the study of DDC, most of them are small …
Although many datasets are introduced to support the study of DDC, most of them are small …
Deep learning-based hard spatial attention for driver in-vehicle action monitoring
Distracted driving is one of the main causes of deaths and injuries in the world. Monitoring
driver behaviors through Driver Action Recognition (DAR) contributes significantly to …
driver behaviors through Driver Action Recognition (DAR) contributes significantly to …
Bidirectional posture-appearance interaction network for driver behavior recognition
Driver behavior recognition has become one of the most important tasks for intelligent
vehicles. This task, however, is very challenging since the background contents in real-world …
vehicles. This task, however, is very challenging since the background contents in real-world …
Swin-fusion: swin-transformer with feature fusion for human action recognition
Human action recognition based on still images is one of the most challenging computer
vision tasks. In the past decade, convolutional neural networks (CNNs) have developed …
vision tasks. In the past decade, convolutional neural networks (CNNs) have developed …
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 …
TransDARC: Transformer-based driver activity recognition with latent space feature calibration
Traditional video-based human activity recognition has experienced remarkable progress
linked to the rise of deep learning, but this effect was slower as it comes to the downstream …
linked to the rise of deep learning, but this effect was slower as it comes to the downstream …