Driver Distraction Identification with an Ensemble of Convolutional Neural Networks
The World Health Organization (WHO) reported 1.25 million deaths yearly due to road traffic accidents worldwide and the number has been continuously increasing over the last few years. Nearly fifth of these accidents are caused by distracted drivers. Existing work of distracted driver detection is c...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2019/4125865 |
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| _version_ | 1832551430498549760 |
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| author | Hesham M. Eraqi Yehya Abouelnaga Mohamed H. Saad Mohamed N. Moustafa |
| author_facet | Hesham M. Eraqi Yehya Abouelnaga Mohamed H. Saad Mohamed N. Moustafa |
| author_sort | Hesham M. Eraqi |
| collection | DOAJ |
| description | The World Health Organization (WHO) reported 1.25 million deaths yearly due to road traffic accidents worldwide and the number has been continuously increasing over the last few years. Nearly fifth of these accidents are caused by distracted drivers. Existing work of distracted driver detection is concerned with a small set of distractions (mostly, cell phone usage). Unreliable ad hoc methods are often used. In this paper, we present the first publicly available dataset for driver distraction identification with more distraction postures than existing alternatives. In addition, we propose a reliable deep learning-based solution that achieves a 90% accuracy. The system consists of a genetically weighted ensemble of convolutional neural networks; we show that a weighted ensemble of classifiers using a genetic algorithm yields a better classification confidence. We also study the effect of different visual elements in distraction detection by means of face and hand localizations, and skin segmentation. Finally, we present a thinned version of our ensemble that could achieve 84.64% classification accuracy and operate in a real-time environment. |
| format | Article |
| id | doaj-art-d8e37d1cba2b4508a65aa53f7c01d95e |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-d8e37d1cba2b4508a65aa53f7c01d95e2025-02-03T06:01:36ZengWileyJournal of Advanced Transportation0197-67292042-31952019-01-01201910.1155/2019/41258654125865Driver Distraction Identification with an Ensemble of Convolutional Neural NetworksHesham M. Eraqi0Yehya Abouelnaga1Mohamed H. Saad2Mohamed N. Moustafa3Department of Computer Science & Engineering, The American University in Cairo, EgyptDepartment of Informatics, Technical University of Munich, GermanyDepartment of Computer and Systems Engineering, Ain Shams University, EgyptDepartment of Computer Science & Engineering, The American University in Cairo, EgyptThe World Health Organization (WHO) reported 1.25 million deaths yearly due to road traffic accidents worldwide and the number has been continuously increasing over the last few years. Nearly fifth of these accidents are caused by distracted drivers. Existing work of distracted driver detection is concerned with a small set of distractions (mostly, cell phone usage). Unreliable ad hoc methods are often used. In this paper, we present the first publicly available dataset for driver distraction identification with more distraction postures than existing alternatives. In addition, we propose a reliable deep learning-based solution that achieves a 90% accuracy. The system consists of a genetically weighted ensemble of convolutional neural networks; we show that a weighted ensemble of classifiers using a genetic algorithm yields a better classification confidence. We also study the effect of different visual elements in distraction detection by means of face and hand localizations, and skin segmentation. Finally, we present a thinned version of our ensemble that could achieve 84.64% classification accuracy and operate in a real-time environment.http://dx.doi.org/10.1155/2019/4125865 |
| spellingShingle | Hesham M. Eraqi Yehya Abouelnaga Mohamed H. Saad Mohamed N. Moustafa Driver Distraction Identification with an Ensemble of Convolutional Neural Networks Journal of Advanced Transportation |
| title | Driver Distraction Identification with an Ensemble of Convolutional Neural Networks |
| title_full | Driver Distraction Identification with an Ensemble of Convolutional Neural Networks |
| title_fullStr | Driver Distraction Identification with an Ensemble of Convolutional Neural Networks |
| title_full_unstemmed | Driver Distraction Identification with an Ensemble of Convolutional Neural Networks |
| title_short | Driver Distraction Identification with an Ensemble of Convolutional Neural Networks |
| title_sort | driver distraction identification with an ensemble of convolutional neural networks |
| url | http://dx.doi.org/10.1155/2019/4125865 |
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