<p> 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. </p><p> 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.</p><p>
| Identifer | oai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:10621991 |
| Date | 21 October 2017 |
| Creators | Ali, Mazin |
| Publisher | Rochester Institute of Technology |
| Source Sets | ProQuest.com |
| Language | English |
| Detected Language | English |
| Type | thesis |
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