# Using Deep Learning for Collecting Data about Museum Visitor Behavior
###### Abstract  
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. cultural heritage fruition; human factors in artificial intelligence; museum visitors analysis; computer vision; machine learning; deep neural networks +
Footnote †: journal: Applied  
reduced, which is certainly a significant advantage over other state-of-the-art technologies. The methodology is based on deep learning, more specifically, methods and techniques for image detection and classification through Convolutional Neural Networks (CNNs) capable of providing excellent performance in terms of accuracy. Our approach can represent the solution to some of the criticalities shown by the other visitor localization technologies in the museum environment. With the proposed setting, the estimate of the visitor position is extremely accurate (on the order of \(10^{-2}\) m). Moreover, the intrusiveness of the proposed approach is minimal. The user is not required to wear additional devices such as active and passive sensors, Personal Digital Assistants (PDAs), smartphones, or portable Graphics Processing Units (GPUs), but a simple badge. Another advantage of our solution is that the coverage is guaranteed at a low cost, through simple commercial RGB cameras or any video surveillance system present in most national and international museums and exhibitions. Lastly, the collected videos can be processed using a free platform such as the Google Colaboratory environment, thus allowing museum curators and staff to save the cost of hardware and system usage. The contributions of this paper are as follows:  
* The analysis of the state of the art to identify the main problems in the timing and tracking of users in indoor environments (i.e., high intrusiveness, low accuracy, high cost, and high consumption);
* The design of a novel, low-cost, and highly accurate system to overcome the aforementioned problems;
* The development of a user timing and tracking system capable of providing data useful both for museum curators and staff (e.g., the possibility to analyze and monitor how visitors enjoy museum objects) and for the visitors themselves (e.g., the possibility to receive personalized suggestions during the visit). We propose a deep learning-based approach to comprehensively and accurately collect visitor experience data. Specifically, we describe in detail the characteristics of its architecture and the experimental results obtained. We also illustrate a case study in a real environment and finally show how the collected data can be stored and used to provide valuable information relating to the behavior of each visitor. This paper is structured as follows.