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Media type:
E-Article
Title:
Driver Distraction Identification with an Ensemble of Convolutional Neural Networks
Contributor:
Eraqi, Hesham M.;
Abouelnaga, Yehya;
Saad, Mohamed H.;
Moustafa, Mohamed N.
imprint:
Hindawi Limited, 2019
Published in:Journal of Advanced Transportation
Language:
English
DOI:
10.1155/2019/4125865
ISSN:
0197-6729;
2042-3195
Origination:
Footnote:
Description:
<jats:p>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<jats:italic> ad hoc</jats:italic> 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.</jats:p>