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.
Highlights
Over the 20 years from 1980 to 2000, the number of licensed drivers in the United States increased by 23.7%, to reach 190.6 million licenses [1]
The numbers of global deaths attributable to Hepatitis and HIV are estimated to be in the order of 1.3 million [4] and 1.1 million [3], respectively, which is almost the same as the number of the people dying yearly due to road traffic accidents
We present a real-time system for driver distraction identification that uses a learnable weighted ensemble of convolutional neural networks (CNNs), a new method for skin segmentation, a challenging distracted driver’s dataset on which we evaluate our proposed solution, and an annotation tool [20] for action labeling that can be used to extend our dataset
Summary
Over the 20 years from 1980 to 2000, the number of licensed drivers in the United States increased by 23.7%, to reach 190.6 million licenses [1]. Despite safety improvements in road and vehicle design, the total number of fatal crashes still increases [1]. The 2017 Global Status Report of the World Health Organization (WHO) reported an estimated 1.25 million yearly deaths due to road traffic accidents worldwide, with up to 50 million people sustaining non-fatal injuries as a result of road traffic accidents [3]. The numbers of global deaths attributable to Hepatitis and HIV are estimated to be in the order of 1.3 million [4] and 1.1 million [3], respectively, which is almost the same as the number of the people dying yearly due to road traffic accidents. Road traffic accidents cause a huge property damage, and the number of road accidents due to distracted driving is steadily increasing
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