Real-Time Detection of Pedestrian Traffic Lights for Visually-Impaired People
Marcelo Cabral Ghilardi, Gabriel Chahade Sibanto Simões, Jônatas Wehrmann, Isabel Harb Manssour, Rodrigo Coelho Barros · 2018
Nowadays there are more than 250 million visually-impaired people worldwide and mobility autonomy in outdoor environments is perhaps the greatest challenge they have to face. More specifically, crossing the street with no human aid is an open problem, since the majority of the pedestrian traffic lights in underdeveloped countries do not provide sound aids. There are very few studies addressing the detection of pedestrian traffic lights based on images acquired by mobile devices, and to the best of our knowledge there is a clear gap in the literature regarding the use of recent state-of-the-art computer vision approaches such as deep neural networks for addressing such a problem. In this paper, we investigate the current state-of-the-art in localization/detection and classification based on deep neural networks, and we present a solution for the detection of pedestrian traffic lights together with their current state for helping visually-impaired people to cross the streets with the aid of their mobile devices. For such, we provide a novel public dataset with 4,399 labeled images of pedestrian traffic lights and we present a detailed comparison among the state-of-the-art methods for classification and localization/detection. We show empirical evidence regarding the feasibility of embedding our approach in mobile devices so it can be used by people with visual impairment.