  In this work, we address the problem of estimating the so-called "Social
Distancing" given a single uncalibrated image in unconstrained scenarios. Our
approach proposes a semi-automatic solution to approximate the homography
matrix between the scene ground and image plane. With the estimated homography,
we then leverage an off-the-shelf pose detector to detect body poses on the
image and to reason upon their inter-personal distances using the length of
their body-parts. Inter-personal distances are further locally inspected to
detect possible violations of the social distancing rules. We validate our
proposed method quantitatively and qualitatively against baselines on public
domain datasets for which we provided groundtruth on inter-personal distances.
Besides, we demonstrate the application of our method deployed in a real
testing scenario where statistics on the inter-personal distances are currently
used to improve the safety in a critical environment.
