# Using Deep Learning for Collecting Data about Museum Visitor Behavior
## 5 Discussion  
In this paper, we reviewed some of the most authoritative and recent works proposed in the literature for indoor localization, focusing on those deployable in museum environments. As we saw in Section 2, each technology has pros and cons. Consequently, we have proposed a solution that requires simple badges and off-the-shelf RGB cameras and relies on deep learning techniques to monitor visitors and their behavior. The source code of the proposed system is available online (see Section 3). The main advantages of such a solution consist in the low cost of the instrumentation and the accuracy ensured by the detection and classification procedures based on the latest generation of Convolutional Neural Networks. As for the first point, the system leverages inexpensive badges and off-the-shelf cameras, which makes it economically viable. As for the second aspect, in Section 3, we have seen that the accuracy of our approach in estimating the visitor position can be pushed on the order of \(10^{-2}\) m. In this regard, it should be noted that the operation of the Faster R-CNN, on which our system relies, does not depend on the number of objects to be recognized within the image. Therefore, the model accuracy is not affected if, in an image, there is only one badge or there are one hundred badges to be recognized. In our experimental trials, we limited ourselves to 12 badges because the SARS-CoV-2 restrictions did not allow us to test our system with more users. Anyway, the performance in terms of Average Precision and Average Recall remained unchanged when there were 12 badges to be recognized within the frame or when there was only one. What could instead be affected is system efficiency, if the number of region proposals in output from the Region Proposal Network significantly increases. We performed our experimental evaluation using an NVIDIA QUADRO P2000 GPU, which allowed us to process four frames per second even when the badges to be recognized were 12. However, it is reasonable to expect that if the badges to be recognized within an image become hundreds, more performing hardware solutions are needed (e.g., based on the use of several GPUs in parallel) if we want to preserve the real-time nature of the process. However, the possible advantages are not limited to those mentioned above. First of all, the intrusiveness of the proposed approach is minimal. It is sufficient for the visitor to wear a simple badge like those provided free of charge by the organizers of events and conferences to be identified and tracked by the proposed technology. Therefore, no active involvement of the visitor is needed, nor are they required to bring additional devices such as active and passive sensors, PDAs, smartphones, or portable GPUs. As a result, our technology is not affected by power consumption issues. Another positive aspect of our solution is its coverage. It is sufficient that in each point of interest there is a commercial camera to make recognition possible. Moreover, the visitor timing and tracking system could also exploit visual data from any video surveillance systems present in most national and international museums and exhibitions. A further benefit of our solution is the possibility of integrating additional functionalities into it. As seen in Section 4.1, our system can efficiently capture other visitor aspects in addition to the badge worn. More specifically, the system can associate the visitor's face with their badge through a simple correlation (see Figure 12). We emphasize once again that our system does not make the recognition of the visitor's face, but only its detection, for privacy reasons. In other terms, the mapping occurs only between face and badge, and not between face and visitor. The detected face can be analyzed to derive further information. It has been shown in the research literature (e.g., see [57; 58]) how it is possible to analyze the user's micro facial expressions to infer information relating to emotions during the visit to predict their valence, arousal, and engagement.