  We consider the problem of localizing visitors in a cultural site from
egocentric (first person) images. Localization information can be useful both
to assist the user during his visit (e.g., by suggesting where to go and what
to see next) and to provide behavioral information to the manager of the
cultural site (e.g., how much time has been spent by visitors at a given
location? What has been liked most?). To tackle the problem, we collected a
large dataset of egocentric videos using two cameras: a head-mounted HoloLens
device and a chest-mounted GoPro. Each frame has been labeled according to the
location of the visitor and to what he was looking at. The dataset is freely
available in order to encourage research in this domain. The dataset is
complemented with baseline experiments performed considering a state-of-the-art
method for location-based temporal segmentation of egocentric videos.
Experiments show that compelling results can be achieved to extract useful
information for both the visitor and the site-manager.
