Driver distraction detection methods: A literature review and framework
Driver inattention and distraction are the main causes of road accidents, many of which
result in fatalities. To reduce road accidents, the development of information systems to …
result in fatalities. To reduce road accidents, the development of information systems to …
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 …
Computer vision‐based recognition of driver distraction: A review
Vehicle crash rates caused by distracted driving have been rising in recent years. Hence,
safety while driving on roads is today a crucial concern across the world. Some of the …
safety while driving on roads is today a crucial concern across the world. Some of the …
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 …
An empirical framework for detecting speaking modes using ensemble classifier
Detecting the speaking modes of human is an important cue in many applications, including
detecting active/inactive participants in video conferencing, monitoring students' attention in …
detecting active/inactive participants in video conferencing, monitoring students' attention in …
General frameworks for anomaly detection explainability: comparative study
Since their inception, AutoEncoders have been very important in representational learning.
They have achieved ground-breaking results in the realm of automated unsupervised …
They have achieved ground-breaking results in the realm of automated unsupervised …
Towards efficient risky driving detection: a benchmark and a semi-supervised model
Q Cheng, H Li, Y Yang, J Ling, X Huang - Sensors, 2024 - mdpi.com
Risky driving is a major factor in traffic incidents, necessitating constant monitoring and
prevention through Intelligent Transportation Systems (ITS). Despite recent progress, a lack …
prevention through Intelligent Transportation Systems (ITS). Despite recent progress, a lack …
Anomaly detection for images using auto-encoder based sparse representation
Q Zhao, F Karray - Image Analysis and Recognition: 17th International …, 2020 - Springer
Anomaly detection is a pattern recognition task that aims at distinguishing abnormal patterns
from normal ones. In this paper, we propose a convolutional auto-encoder based model to …
from normal ones. In this paper, we propose a convolutional auto-encoder based model to …
[PDF][PDF] Integration of ensemble variant cnn with architecture modified lstm for distracted driver detection
Z Boucetta, A El Fazziki, M El Adnani - Int. J. Adv. Comput. Sci. Appl, 2022 - papers.ssrn.com
Driver decisions and behaviors are the major factors in on-road driving safety. Most
significantly, traffic injuries and accidents are reduced using the accurate driver behavior …
significantly, traffic injuries and accidents are reduced using the accurate driver behavior …