Using Deep Learning for Collecting Data about Museum Visitor Behavior
Nowadays, technology makes it possible to admire objects and artworks exhibited all over the world remotely. We have been able to appreciate this convenience even more in the last period, in which the pandemic has forced us into our homes for a long time. However, visiting art sites in person remain...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/2/533 |
| _version_ | 1797496184708792320 |
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| author | Alessio Ferrato Carla Limongelli Mauro Mezzini Giuseppe Sansonetti |
| author_facet | Alessio Ferrato Carla Limongelli Mauro Mezzini Giuseppe Sansonetti |
| author_sort | Alessio Ferrato |
| collection | DOAJ |
| description | Nowadays, technology makes it possible to admire objects and artworks exhibited all over the world remotely. We have been able to appreciate this convenience even more in the last period, in which the pandemic has forced us into our homes for a long time. However, visiting art sites in person remains a truly unique experience. Even during on-site visits, technology can help make them much more satisfactory, by assisting visitors during the fruition of cultural and artistic resources. To this aim, it is necessary to monitor the active user for acquiring information about their behavior. We, therefore, need systems able to monitor and analyze visitor behavior. The literature proposes several techniques for the timing and tracking of museum visitors. In this article, we propose a novel approach to indoor tracking that can represent a promising and non-expensive solution for some of the critical issues that remain. In particular, the system we propose relies on low-cost equipment (i.e., simple badges and off-the-shelf RGB cameras) and harnesses one of the most recent deep neural networks (i.e., Faster R-CNN) for detecting specific objects in an image or a video sequence with high accuracy. An experimental evaluation performed in a real scenario, namely, the “Exhibition of Fake Art” at Roma Tre University, allowed us to test our system on site. The collected data has proven to be accurate and helpful for gathering insightful information on visitor behavior. |
| first_indexed | 2024-03-10T03:00:05Z |
| format | Article |
| id | doaj.art-aed12d5bc4b44994a9a823e1b6c6a099 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T03:00:05Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-aed12d5bc4b44994a9a823e1b6c6a0992023-11-23T12:48:37ZengMDPI AGApplied Sciences2076-34172022-01-0112253310.3390/app12020533Using Deep Learning for Collecting Data about Museum Visitor BehaviorAlessio Ferrato0Carla Limongelli1Mauro Mezzini2Giuseppe Sansonetti3Department of Engineering, Roma Tre University, 00146 Rome, ItalyDepartment of Engineering, Roma Tre University, 00146 Rome, ItalyDepartment of Education, Roma Tre University, 00185 Rome, ItalyDepartment of Engineering, Roma Tre University, 00146 Rome, ItalyNowadays, technology makes it possible to admire objects and artworks exhibited all over the world remotely. We have been able to appreciate this convenience even more in the last period, in which the pandemic has forced us into our homes for a long time. However, visiting art sites in person remains a truly unique experience. Even during on-site visits, technology can help make them much more satisfactory, by assisting visitors during the fruition of cultural and artistic resources. To this aim, it is necessary to monitor the active user for acquiring information about their behavior. We, therefore, need systems able to monitor and analyze visitor behavior. The literature proposes several techniques for the timing and tracking of museum visitors. In this article, we propose a novel approach to indoor tracking that can represent a promising and non-expensive solution for some of the critical issues that remain. In particular, the system we propose relies on low-cost equipment (i.e., simple badges and off-the-shelf RGB cameras) and harnesses one of the most recent deep neural networks (i.e., Faster R-CNN) for detecting specific objects in an image or a video sequence with high accuracy. An experimental evaluation performed in a real scenario, namely, the “Exhibition of Fake Art” at Roma Tre University, allowed us to test our system on site. The collected data has proven to be accurate and helpful for gathering insightful information on visitor behavior.https://www.mdpi.com/2076-3417/12/2/533cultural heritage fruitionhuman factors in artificial intelligencemuseum visitors analysiscomputer visionmachine learningdeep neural networks |
| spellingShingle | Alessio Ferrato Carla Limongelli Mauro Mezzini Giuseppe Sansonetti Using Deep Learning for Collecting Data about Museum Visitor Behavior Applied Sciences cultural heritage fruition human factors in artificial intelligence museum visitors analysis computer vision machine learning deep neural networks |
| title | Using Deep Learning for Collecting Data about Museum Visitor Behavior |
| title_full | Using Deep Learning for Collecting Data about Museum Visitor Behavior |
| title_fullStr | Using Deep Learning for Collecting Data about Museum Visitor Behavior |
| title_full_unstemmed | Using Deep Learning for Collecting Data about Museum Visitor Behavior |
| title_short | Using Deep Learning for Collecting Data about Museum Visitor Behavior |
| title_sort | using deep learning for collecting data about museum visitor behavior |
| topic | cultural heritage fruition human factors in artificial intelligence museum visitors analysis computer vision machine learning deep neural networks |
| url | https://www.mdpi.com/2076-3417/12/2/533 |
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