Tracking museum visitors may provide useful insights about them, thus enabling curators and personnel to better manage the flows and the arrangement of the museum's works and to develop a recommender system as well. Tracking of visits in a museum environment is an expensive task if performed without automatic tracking systems. For this reason, many automatic tracking systems are proposed in the research literature. However, some of them are expensive (e.g., systems based on light detection and ranging (LIDAR) technology), or require active collaboration from visitors (e.g., systems based on wearable devices). In this work, we propose a deep learning object detection approach to the problem of tracking visitors. The proposed system can accurately detect specific objects in videos, thus allowing for the careful measurement of the spatial and temporal movements of a visitor in a museum scenario. The system requires only off-the-shelf inexpensive devices and deep learning models for object detection and recognition.
Mezzini, M., Limongelli, C., Sansonetti, G., De Medio, C. (2020). Tracking Museum Visitors through Convolutional Object Detectors. In UMAP '20 Adjunct: Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization (pp.352-355). New York, NY : Association for Computing Machinery [10.1145/3386392.3399282].