Driver behaviour detection using 1D convolutional neural networks
Mohammad Shahverdy, Mahmood Fathy, Reza Berangi, Mohammad Sabokrou
Abstract
Abstract Driver behaviour is an important factor in road safety. Computer vision techniques have been widely used to monitor the driver behaviour. The violation of privacy and the possibility of spoofing are two continuing challenges in camera‐based systems. To address these challenges, we propose an efficient approach to monitor and detect driver behaviour based on movement characteristics of the vehicle rather than the visual features of the driver. The main goal of this paper is to classify the driver behaviour into five classes: safe, distracted, aggressive, drunk, and drowsy driving. A lightweight 1D Convolutional Neural Network with high efficiency and low computational complexity is suggested to classify the driver behaviour. Experimental results confirm that our method could successfully classify behaviours of a driver with accuracy of 99.999%.