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 in 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.

Comment: arXiv admin note: substantial text overlap with arXiv:1706.09498

Document type: Article

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The different versions of the original document can be found in:

http://downloads.hindawi.com/journals/jat/2019/4125865.xml,
http://dx.doi.org/10.1155/2019/4125865 under the license cc-by
https://doaj.org/toc/0197-6729,
https://doaj.org/toc/2042-3195 under the license http://creativecommons.org/licenses/by/4.0/
https://www.hindawi.com/journals/jat/2019/4125865,
http://downloads.hindawi.com/journals/jat/2019/4125865.pdf,
https://academic.microsoft.com/#/detail/2912346386
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Published on 01/01/2019

Volume 2019, 2019
DOI: 10.1155/2019/4125865
Licence: Other

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