Abstract. Individuals with sight impairments rely heavily on various types of travel-aid when navigating their ways across their neighborhoods. Recently, there have been many breakthrough technologies that focus on the visually impaired by providing solutions such as wearable bands and optical wearable devices. However, such technologies are costly and not suited for the general market. Others have started investigating smartphone applications as a much more widely available solution but with limited applicability on outdoor barriers and obstacles that these groups of people face in their day to day journeys. In this work, we propose GeoNotify, a smartphone application which is tailored to detect unexpected temporary obstacles that could cause injury to visually impaired people. We present how advances in Convolutional Neural Networks merged with crowd-sourcing methodologies could be used to build more accurate models capable of recognizing wide representations of the real-world obstacles.
Authors: Edward Kim (University of Washington), Joshua Sterner (University of Washington), Afra Mashhadi (University of Washington),
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