  The recent pandemic emergency raised many challenges regarding the
countermeasures aimed at containing the virus spread, and constraining the
minimum distance between people resulted in one of the most effective
strategies. Thus, the implementation of autonomous systems capable of
monitoring the so-called social distance gained much interest. In this paper,
we aim to address this task leveraging a single RGB frame without additional
depth sensors. In contrast to existing single-image alternatives failing when
ground localization is not available, we rely on single image depth estimation
to perceive the 3D structure of the observed scene and estimate the distance
between people. During the setup phase, a straightforward calibration
procedure, leveraging a scale-aware SLAM algorithm available even on consumer
smartphones, allows us to address the scale ambiguity affecting single image
depth estimation. We validate our approach through indoor and outdoor images
employing a calibrated LiDAR + RGB camera asset. Experimental results highlight
that our proposal enables sufficiently reliable estimation of the
inter-personal distance to monitor social distancing effectively. This fact
confirms that despite its intrinsic ambiguity, if appropriately driven single
image depth estimation can be a viable alternative to other depth perception
techniques, more expensive and not always feasible in practical applications.
Our evaluation also highlights that our framework can run reasonably fast and
comparably to competitors, even on pure CPU systems. Moreover, its practical
deployment on low-power systems is around the corner.
