Driver cellphone usage detection using wavelet scattering and convolutional neural networks

Document Type : Original Article

Authors

Virtual Reality Laboratory, K.N. Toosi University of Technology, Tehran, Iran

Abstract

This paper provides an automated system based on machine learning and computer vision to detect cellphone usage during driving. We used Wavelet Scattering Networks, which is a simple and efficient type of architecture. The presented model is straightforward and compact and requires little hyper-parameter tuning. The speed of this model is similar to the Convolutional Neural Networks. We monitored the driver from two viewpoints: a frontal view of the driver’s face and a side view of the driver’s whole body. We created a new dataset for the first viewpoint, and used a publicly available dataset for the second viewpoint. Our model achieved the test accuracy of 91% for our new dataset and 99% for the publicly available one.

Keywords

AUT Journal of Mathematics and Computing

Articles in Press, Accepted Manuscript
Available Online from 01 September 2023

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