Driver Distraction Identification with an Ensemble of Convolutional Neural Networks

Author:

Eraqi Hesham M.1ORCID,Abouelnaga Yehya2ORCID,Saad Mohamed H.3ORCID,Moustafa Mohamed N.1ORCID

Affiliation:

1. Department of Computer Science & Engineering, The American University in Cairo, Egypt

2. Department of Informatics, Technical University of Munich, Germany

3. Department of Computer and Systems Engineering, Ain Shams University, Egypt

Abstract

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.

Funder

Deutsche Forschungsgemeinschaft

Publisher

Hindawi Limited

Subject

Strategy and Management,Computer Science Applications,Mechanical Engineering,Economics and Econometrics,Automotive Engineering

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