Measurement and analysis of visitors' trajectories in crowded museums
This work addresses facility management and art preservation challenges for crowded museums, but it is incremental as it applies existing IoT and AI methods to a new domain.
The authors tackled the problem of measuring visitor dynamics in crowded museums by developing an IoT-based system with AI models to reconstruct trajectories, enabling insights for facility management and art preservation, as demonstrated in a use case at the Galleria Borghese in Rome.
We tackle the issue of measuring and analyzing the visitors' dynamics in crowded museums. We propose an IoT-based system -- supported by artificial intelligence models -- to reconstruct the visitors' trajectories throughout the museum spaces. Thanks to this tool, we are able to gather wide ensembles of visitors' trajectories, allowing useful insights for the facility management and the preservation of the art pieces. Our contribution comes with one successful use case: the Galleria Borghese in Rome, Italy.