Faculty of Computing

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Has files: YesSubject: search.filters.subject.visually impaired

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    Learning platform for visually impaired children through artificial intelligence and computer vision
    (IEEE, 2018-02-19) Balasuriya, B. K; Lokuhettiarachchi, N. P; Ranasinghe, A. R. M. D. N; Shiwantha, K. D. C; Jayawardena, C
    The topic Visual Disabilities and Computer Vision are the most researched topics of recent years. Researchers have been trying to combine two topics to create most usable systems to the visually disabled to aid them in their day to day tasks. In this research, we are trying to create an application which is targeting children between the age of 6-14 who suffers from visual disabilities to aid them in their primary learning task of learning to identify objects without a supervision of a third-party. We are trying to achieve this task by combining latest advancements of Computer Vision and Artificial Intelligence technologies by using Deep Region Based Convolutional Networks (R-CNN), Recurrent Neural Networks (RNN) and Speech models to provide an interactive learning experience to such individuals. The paper discusses.
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    EyeVista: An assistive wearable device for visually impaired sprint athletes
    (IEEE, 2016-12-16) Peiris, H; Kulasekara, C; Wijesinghe, H; Kothalawala, B; Walgampaya, N; Kasthurirathna, D
    On-going progressions of Information Technology increase the scope for computer vision-based interventions to facilitate efficient and promising technology for people with disabilities. This project aims to develop a wearable navigational assistive device, titled EyeVista, to facilitate visually impaired sprint athletes. It is a lightweight, easy-to-use, customizable and low-cost wearable jacket built-in with off-the-shelf based on computer vision techniques. Synthesis of research initially reflects the impact of the main barriers of a human guide and how to break down such barriers. In doing so, we hope to introduce an alternative to the current practice of having a human guide for blind athletes, overcoming the shortcomings of it. The designed system uses Raspberry Pi single board computer to process the real-time image captured by Raspberry Pi camera module to navigate the athletes within the assigned track and to avoid collisions. As a result, we believe the project EyeVista will empower the visually impaired sprint athletes to enhance their performance by easing their mobility by allowing the user to move within their relevant track lanes and avoid collisions without the support of a human guide and enhance the independence, safety along with the quality of life.