Classification of a Pedestrian’s Behaviour Using Dual Deep Neural Networks

Abstract

Vulnerable road user safety is of paramount importance as transport moves towards fully autonomous driving. The research question posed by this research is of how can we train a computer to be able to see and perceive a pedestrian’s movement. This work presents a dual network architecture, trained in tandem, which is capable of classifying the behaviour of a pedestrian from a single image with no prior context. The results show that the most successful network was able to achieve a correct classification accuracy of 94.3% when classifying images based on their behaviour. This shows the use of a novel data fusion method for pedestrian images and human poses. Having a network with these capabilities is important for the future of transport, as it will allow vehicles to correctly perceive the intention of pedestrians crossing the street, and will ultimately lead to fewer pedestrian casualties on our roads.