Detection and Recognition of Barriers in Egocentric Images for Safe Urban Sidewalks

Abstract

The impact of walking in modern cities has proven to be quite significant with many advantages especially in the fields of environment and citizens’ health. Although society is trying to promote it as the cheapest and most sustainable means of transportation, many road accidents have involved pedestrians and cyclists in the recent years. The frequent presence of various obstacles on urban sidewalks puts the lives of citizens in danger. Their immediate detection and removal are of great importance for maintaining clean and safe access to infrastructure of urban environments. Following the great success of egocentric applications that take advantage of the uninterrupted use of smartphone devices to address serious problems that concern humanity, we aim to develop methodologies for detecting barriers and other dangerous obstacles encountered by pedestrians on urban sidewalks. For this purpose a dedicated image dataset is generated and used as the basis for analyzing the performance of different methods in detecting and recognizing different types of obstacle using three different architectures of deep learning algorithms. The high accuracy of the experimental results shows that the development of egocentric applications can successfully help to maintain the safety and cleanliness of sidewalks and at the same time to reduce pedestrian accidents.