  The COVID-19 virus has caused a global pandemic since March 2020. The World
Health Organization (WHO) has provided guidelines on how to reduce the spread
of the virus and one of the most important measures is social distancing.
Maintaining a minimum of one meter distance from other people is strongly
suggested to reduce the risk of infection. This has created a strong interest
in monitoring the social distances either as a safety measure or to study how
the measures have affected human behavior and country-wise differences in this.
The need for automatic social distance estimation algorithms is evident, but
there is no suitable test benchmark for such algorithms. Collecting images with
measured ground-truth pair-wise distances between all the people using
different camera settings is cumbersome. Furthermore, performance evaluation
for social distance estimation algorithms is not straightforward and there is
no widely accepted evaluation protocol. In this paper, we provide a dataset of
varying images with measured pair-wise social distances under different camera
positionings and focal length values. We suggest a performance evaluation
protocol and provide a benchmark to easily evaluate social distance estimation
algorithms. We also propose a method for automatic social distance estimation.
Our method takes advantage of object detection and human pose estimation. It
can be applied on any single image as long as focal length and sensor size
information are known. The results on our benchmark are encouraging with 92%
human detection rate and only 28.9% average error in distance estimation among
the detected people.
