Distracted Driver Detection with Deep Convolutional Neural Network

Abstract

According to the World Health Organization (WHO), over 1.3 million deaths occur worldwide each year due to traffic accidents alone. This figure elevates traffic mishaps to be the eight leading cause of death. According to another study the United States National Highway Traffic Safety Administration (NHTSA), the major cause of road deaths and injury is distracted drivers. Motivated by recent advancement of deep learning and computer vision in predicting drivers’ behaviour, this paper attempts to investigate the optimal deep learning network architecture to accurately detect distracted drivers over visual feed. Specifically, a thorough evaluation and detailed benchmark comparisons of pretrained deep convolutional neural network is carried out. Results indicate that the proposed VGG16network architecture is capable of achieving 96% accuracy on the test dataset images.