Abstract
We present in this paper the state of the art and an analysis of recent research work and achievements performed in the domain of AI-based and vision-based systems for helping blind and visually impaired people (BVIP). We start by highlighting the recent and tremendous importance that AI has acquired following the use of convolutional neural networks (CNN) and their ability to solve image classification tasks efficiently. After that, we also note that VIP have high expectations about AI-based systems as a possible way to ease the perception of their environment and to improve their everyday life. Then, we set the scope of our survey: we concentrate our investigations on the use of CNN or related methods in a vision-based system for helping BVIP. We analyze the existing surveys, and we study the current work (a selection of 30 case studies) using several dimensions such as acquired data, learned models, and human–computer interfaces. We compare the different approaches, and conclude by analyzing future trends in this domain.
Highlights
According to the World Health Organization, 285 million people suffer from important sight loss (39 million blind and 246 million with impaired vision), and the figures will keep rising as populations grow older
White canes and guide dogs have acted as walking assistants, but recent advances in deep learning and computer-vision technologies have broadened the spectrum of possibilities
We studied recent advances in the field of AI techniques for developing assistive technologies for blind and visually impaired people (BVIP)
Summary
According to the World Health Organization, 285 million people suffer from important sight loss (39 million blind and 246 million with impaired vision), and the figures will keep rising as populations grow older. Several surveys have been conducted in recent years on assistive technologies for BVIP, and the range of technologies available that could be used to develop them Some of those articles were read when preparing this paper, to understand the different ways this subject has been addressed in the past. In their articles, Bhowmick and Hazarika [4] and Khan et al [5] provided detailed descriptions of their research methodologies, highlighting the connections between the different fields pertaining to assistive technologies for BVIP. The results summarized in tables with their percentage of occurrence
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