Dissertation/Thesis Abstract

360° View Camera Based Visual Assistive Technology for Contextual Scene Information
by Ali, Mazin, M.S., Rochester Institute of Technology, 2017, 54; 10621991
Abstract (Summary)

In this research project, a system is proposed to aid the visually impaired by providing partial contextual information of the surroundings using 360° view camera combined with deep learning is proposed. The system uses a 360° view camera with a mobile device to capture surrounding scene information and provide contextual information to the user in the form of audio. The system could also be used for other applications such as logo detection which visually impaired users can use for shopping assistance.

The scene information from the spherical camera feed is classified by identifying objects that contain contextual information of the scene. That is achieved using convolutional neural networks (CNN) for classification by leveraging CNN transfer learning properties using the pre-trained VGG-19 network. There are two challenges related to this paper, a classification and a segmentation challenge. As an initial prototype, we have experimented with general classes such restaurants, coffee shops and street signs. We have achieved a 92.8% classification accuracy in this research project.

Indexing (document details)
Advisor: Sahin, Ferat
Commitee: Monteiro, Sildomar, Tsouri, Gill
School: Rochester Institute of Technology
Department: Electrical Engineering
School Location: United States -- New York
Source: MAI 56/06M(E), Masters Abstracts International
Subjects: Computer Engineering, Biomedical engineering, Electrical engineering, Robotics, Artificial intelligence
Keywords: Assistive technology, Convolutional neural network, Deep learning, Machine learning, Visual impairment
Publication Number: 10621991
ISBN: 978-0-355-20378-3
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