A real time deep learning based driver monitoring system
Road traffic accidents almost kill 1.35 million people around the world. Most of these accidents take place in low and middle-income countries and costs them around 3% of their gross domestic product. Around 20% of the traffic accidents are attributed to distracted drowsy drivers. Many detection...
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my.iium.irep.948012021-12-15T08:38:05Z http://irep.iium.edu.my/94801/ A real time deep learning based driver monitoring system Mohd Hanafi, Mohamad Faris Fitri Md. Nasir, Mohammad Sukri Faiz Wani, Sharyar Abdulghafor, Rawad Abdulkhaleq Abdulmolla Gulzar, Yonis Hamid, Yasir T Technology (General) Road traffic accidents almost kill 1.35 million people around the world. Most of these accidents take place in low and middle-income countries and costs them around 3% of their gross domestic product. Around 20% of the traffic accidents are attributed to distracted drowsy drivers. Many detection systems have been designed to alert the drivers to reduce the huge number of accidents. However, most of them are based on specialized hardware integrated with the vehicle. As such the installation becomes expensive and unaffordable especially in the low- and middle-income sector. In the last decade, smartphones have become essential and affordable. Some researchers have focused on developing mobile engines based on machine learning algorithms for detecting driver drowsiness. However, most of them either suffer from platform dependence or intermittent detection issues. This research aims at developing a real time distracted driver monitoring engine while being operating system agnostic using deep learning. It employs a CNN for detection, feature extraction, image classification and alert generation. The system training will use both openly available and privately gathered data. IIUM 2021-07-10 Article PeerReviewed application/pdf en http://irep.iium.edu.my/94801/1/94801_A%20real%20time%20deep%20learning%20based%20driver%20monitoring%20system.pdf Mohd Hanafi, Mohamad Faris Fitri and Md. Nasir, Mohammad Sukri Faiz and Wani, Sharyar and Abdulghafor, Rawad Abdulkhaleq Abdulmolla and Gulzar, Yonis and Hamid, Yasir (2021) A real time deep learning based driver monitoring system. International Journal on Perceptive and Cognitive Computing (IJPCC), 7 (1). pp. 79-84. ISSN 2462-229X https://journals.iium.edu.my/kict/index.php/IJPCC/article/view/224 |
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T Technology (General) Mohd Hanafi, Mohamad Faris Fitri Md. Nasir, Mohammad Sukri Faiz Wani, Sharyar Abdulghafor, Rawad Abdulkhaleq Abdulmolla Gulzar, Yonis Hamid, Yasir A real time deep learning based driver monitoring system |
description |
Road traffic accidents almost kill 1.35 million people around the world. Most of these accidents
take place in low and middle-income countries and costs them around 3% of their gross domestic product.
Around 20% of the traffic accidents are attributed to distracted drowsy drivers. Many detection systems have
been designed to alert the drivers to reduce the huge number of accidents. However, most of them are
based on specialized hardware integrated with the vehicle. As such the installation becomes expensive and
unaffordable especially in the low- and middle-income sector. In the last decade, smartphones have become
essential and affordable. Some researchers have focused on developing mobile engines based on machine
learning algorithms for detecting driver drowsiness. However, most of them either suffer from platform
dependence or intermittent detection issues. This research aims at developing a real time distracted driver
monitoring engine while being operating system agnostic using deep learning. It employs a CNN for
detection, feature extraction, image classification and alert generation. The system training will use both
openly available and privately gathered data. |
format |
Article |
author |
Mohd Hanafi, Mohamad Faris Fitri Md. Nasir, Mohammad Sukri Faiz Wani, Sharyar Abdulghafor, Rawad Abdulkhaleq Abdulmolla Gulzar, Yonis Hamid, Yasir |
author_facet |
Mohd Hanafi, Mohamad Faris Fitri Md. Nasir, Mohammad Sukri Faiz Wani, Sharyar Abdulghafor, Rawad Abdulkhaleq Abdulmolla Gulzar, Yonis Hamid, Yasir |
author_sort |
Mohd Hanafi, Mohamad Faris Fitri |
title |
A real time deep learning based driver monitoring system |
title_short |
A real time deep learning based driver monitoring system |
title_full |
A real time deep learning based driver monitoring system |
title_fullStr |
A real time deep learning based driver monitoring system |
title_full_unstemmed |
A real time deep learning based driver monitoring system |
title_sort |
real time deep learning based driver monitoring system |
publisher |
IIUM |
publishDate |
2021 |
url |
http://irep.iium.edu.my/94801/1/94801_A%20real%20time%20deep%20learning%20based%20driver%20monitoring%20system.pdf http://irep.iium.edu.my/94801/ https://journals.iium.edu.my/kict/index.php/IJPCC/article/view/224 |
_version_ |
1720436655974776832 |
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13.201949 |