  Equipping visitors of a cultural site with a wearable device allows to easily
collect information about their preferences which can be exploited to improve
the fruition of cultural goods with augmented reality. Moreover, egocentric
video can be processed using computer vision and machine learning to enable an
automated analysis of visitors' behavior. The inferred information can be used
both online to assist the visitor and offline to support the manager of the
site. Despite the positive impact such technologies can have in cultural
heritage, the topic is currently understudied due to the limited number of
public datasets suitable to study the considered problems. To address this
issue, in this paper we propose EGOcentric-Cultural Heritage (EGO-CH), the
first dataset of egocentric videos for visitors' behavior understanding in
cultural sites. The dataset has been collected in two cultural sites and
includes more than $27$ hours of video acquired by $70$ subjects, with labels
for $26$ environments and over $200$ different Points of Interest. A large
subset of the dataset, consisting of $60$ videos, is associated with surveys
filled out by real visitors. To encourage research on the topic, we propose $4$
challenging tasks (room-based localization, point of interest/object
recognition, object retrieval and survey prediction) useful to understand
visitors' behavior and report baseline results on the dataset.
