Driver Distraction Identification with an Ensemble of Convolutional Neural Networks

Author's Department

Computer Science & Engineering Department

Second Author's Department

Computer Science & Engineering Department

Find in your Library


All Authors

Hesham M. Eraqi; Yehya Abouelnaga; Mohamed H. Saad; Mohamed N. Moustafa

Document Type

Research Article

Publication Title

Journal of Advanced Transportation

Publication Date





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.

First Page


Last Page


This document is currently not available here.