  Social distancing has been proven as an effective measure against the spread
of the infectious COronaVIrus Disease 2019 (COVID-19). However, individuals are
not used to tracking the required 6-feet (2-meters) distance between themselves
and their surroundings. An active surveillance system capable of detecting
distances between individuals and warning them can slow down the spread of the
deadly disease. Furthermore, measuring social density in a region of interest
(ROI) and modulating inflow can decrease social distancing violation occurrence
chance.
  On the other hand, recording data and labeling individuals who do not follow
the measures will breach individuals' rights in free-societies. Here we propose
an Artificial Intelligence (AI) based real-time social distancing detection and
warning system considering four important ethical factors: (1) the system
should never record/cache data, (2) the warnings should not target the
individuals, (3) no human supervisor should be in the detection/warning loop,
and (4) the code should be open-source and accessible to the public. Against
this backdrop, we propose using a monocular camera and deep learning-based
real-time object detectors to measure social distancing. If a violation is
detected, a non-intrusive audio-visual warning signal is emitted without
targeting the individual who breached the social distancing measure. Also, if
the social density is over a critical value, the system sends a control signal
to modulate inflow into the ROI. We tested the proposed method across
real-world datasets to measure its generality and performance. The proposed
method is ready for deployment, and our code is open-sourced.
