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 sy...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English English |
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World Academy of Research in Science and Engineering
2019
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| Online Access: | http://irep.iium.edu.my/77445/ http://irep.iium.edu.my/77445/2/IJATCSE%20Scopus%20Proof.pdf http://irep.iium.edu.my/77445/15/77445_A%20real%20time%20deep%20learning%20based%20driver.pdf |
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| author | Wani, Sharyar Fitri, Mohamad Faris Abdulghafor, Rawad Abdulkhaleq Abdulmolla Faiz, Mohammad Syukri Sembok, Tengku Mohd |
| author_facet | Wani, Sharyar Fitri, Mohamad Faris Abdulghafor, Rawad Abdulkhaleq Abdulmolla Faiz, Mohammad Syukri Sembok, Tengku Mohd |
| author_sort | Wani, Sharyar |
| building | IIUM Repository |
| collection | Online Access |
| 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 machine learning for detection, feature extraction, image classification and alert generation. The system training will use both openly available and privately gathered data. |
| first_indexed | 2025-11-14T17:40:53Z |
| format | Article |
| id | iium-77445 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T17:40:53Z |
| publishDate | 2019 |
| publisher | World Academy of Research in Science and Engineering |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-774452021-12-15T08:04:40Z http://irep.iium.edu.my/77445/ A real time deep learning based driver monitoring system Wani, Sharyar Fitri, Mohamad Faris Abdulghafor, Rawad Abdulkhaleq Abdulmolla Faiz, Mohammad Syukri Sembok, Tengku Mohd QA75 Electronic computers. Computer science 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 machine learning for detection, feature extraction, image classification and alert generation. The system training will use both openly available and privately gathered data. World Academy of Research in Science and Engineering 2019 Article PeerReviewed application/pdf en http://irep.iium.edu.my/77445/2/IJATCSE%20Scopus%20Proof.pdf application/pdf en http://irep.iium.edu.my/77445/15/77445_A%20real%20time%20deep%20learning%20based%20driver.pdf Wani, Sharyar and Fitri, Mohamad Faris and Abdulghafor, Rawad Abdulkhaleq Abdulmolla and Faiz, Mohammad Syukri and Sembok, Tengku Mohd (2019) A real time deep learning based driver monitoring system. International Journal of Advanced Trends in Computer Science and Engineering, 7 (1). E-ISSN 2278-3091 http://www.warse.org/IJATCSE/ |
| spellingShingle | QA75 Electronic computers. Computer science T Technology (General) Wani, Sharyar Fitri, Mohamad Faris Abdulghafor, Rawad Abdulkhaleq Abdulmolla Faiz, Mohammad Syukri Sembok, Tengku Mohd A real time deep learning based driver monitoring system |
| title | 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_short | A real time deep learning based driver monitoring system |
| title_sort | real time deep learning based driver monitoring system |
| topic | QA75 Electronic computers. Computer science T Technology (General) |
| url | http://irep.iium.edu.my/77445/ http://irep.iium.edu.my/77445/ http://irep.iium.edu.my/77445/2/IJATCSE%20Scopus%20Proof.pdf http://irep.iium.edu.my/77445/15/77445_A%20real%20time%20deep%20learning%20based%20driver.pdf |