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Personalized Real-Time Virtual Tours In Places With Cultural Interest

Personalized Real-Time Virtual Tours In Places With Cultural Interest

Emmanouil Skondras, Konstantina Siountri, Angelos Michalas, Dimitrios D. Vergados
Copyright: © 2019 |Volume: 3 |Issue: 1 |Pages: 17
ISSN: 2473-5345|EISSN: 2473-5337|EISBN13: 9781522568728|DOI: 10.4018/IJCMHS.2019010104
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MLA

Skondras, Emmanouil, et al. "Personalized Real-Time Virtual Tours In Places With Cultural Interest." IJCMHS vol.3, no.1 2019: pp.52-68. http://doi.org/10.4018/IJCMHS.2019010104

APA

Skondras, E., Siountri, K., Michalas, A., & Vergados, D. D. (2019). Personalized Real-Time Virtual Tours In Places With Cultural Interest. International Journal of Computational Methods in Heritage Science (IJCMHS), 3(1), 52-68. http://doi.org/10.4018/IJCMHS.2019010104

Chicago

Skondras, Emmanouil, et al. "Personalized Real-Time Virtual Tours In Places With Cultural Interest," International Journal of Computational Methods in Heritage Science (IJCMHS) 3, no.1: 52-68. http://doi.org/10.4018/IJCMHS.2019010104

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Abstract

Virtual tours using drones enhance the experience the users perceive from a place with cultural interest. Drones equipped with 360o cameras perform real-time video streaming of the cultural sites. The user preferences about each monument type should be considered in order to decide the appropriate flying route for the drone. This article describes a scheme for supporting personalized real-time virtual tours at sites with cultural interest using drones. The user preferences are modeled using the MPEG-21 and the MPEG-7 standards, while Web Ontology Language (OWL) ontologies are used for metadata structure and semantics. The Metadata-Aware Analytic Network Process (MANP) algorithm is proposed in order to weigh the user preferences for each monument type. Subsequently, the Trapezoidal Fuzzy Topsis for Heritage Route Selection (TFT-HRS) algorithm ranks the candidate heritage routes. Finally, after each virtual tour, the user preferences metadata are updated in order in order the scheme to continuously learn about the user preferences.

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