The main campus of Virginia Polytechnic Institute and State University (Virginia Tech) has more than 120 buildings. The campus visitors face problems recognizing a building, finding a building, obtaining directions from one building to another, and getting information about a building. The exploratory development research described herein resulted in an iPhone / iPad software application (app) named VTQuestAR that provides assistance to the campus visitors by using the Augmented Reality (AR) technology. The Machine Learning (ML) technology is used to recognize a sample of 31 campus buildings in real-time. The VTQuestAR app enables the user to have a visual interactive experience with those 31 campus buildings by superimposing building information on top of the building picture shown through the camera. The app also enables the user to get directions from the current location or a building to another building displayed on a 2D map as well as an AR map. The user can perform complex searches on 122 campus buildings by building name, description, abbreviation, category, address, and year built. The app enables the user to take multimedia notes during a campus visit. Our exploratory development research illustrates the feasibility of using AR and ML in providing much more effective assistance to visitors of any organization. / Master of Science / The main campus of Virginia Polytechnic Institute and State University (Virginia Tech) has more than 120 buildings. The campus visitors face problems recognizing a building, finding a building, obtaining directions from one building to another, and getting information about a building. The exploratory development research described herein resulted in an iPhone / iPad software application named VTQuestAR that provides assistance to the campus visitors by using the Augmented Reality (AR) and Machine Learning (ML) technologies. Our research illustrates the feasibility of using AR and ML in providing much more effective assistance to visitors of any organization.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/101790 |
| Date | 07 January 2021 |
| Creators | Yao, Zhennan |
| Contributors | Computer Science, Balci, Osman, Zhang, Liqing, Barkhi, Reza |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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