  With the relaxation of the containment measurements around the globe,
monitoring the social distancing in crowded public places is of grate
importance to prevent a new massive wave of COVID-19 infections. Recent works
in that matter have limited themselves by detecting social distancing in
corridors up to small crowds by detecting each person individually considering
the full body in the image. In this work, we propose a new framework for
monitoring the social-distance using end-to-end Deep Learning, to detect crowds
violating the social-distance in wide areas where important occlusions may be
present. Our framework consists in the creation of a new ground truth based on
the ground truth density maps and the proposal of two different solutions, a
density-map-based and a segmentation-based, to detect the crowds violating the
social-distance constrain. We assess the results of both approaches by using
the generated ground truth from the PET2009 and CityStreet datasets. We show
that our framework performs well at providing the zones where people are not
following the social-distance even when heavily occluded or far away from one
camera.
