  Social distancing, an essential public health measure to limit the spread of
contagious diseases, has gained significant attention since the outbreak of the
COVID-19 pandemic. In this work, the problem of visual social distancing
compliance assessment in busy public areas, with wide field-of-view cameras, is
considered. A dataset of crowd scenes with people annotations under a bird's
eye view (BEV) and ground truth for metric distances is introduced, and several
measures for the evaluation of social distance detection systems are proposed.
A multi-branch network, BEV-Net, is proposed to localize individuals in world
coordinates and identify high-risk regions where social distancing is violated.
BEV-Net combines detection of head and feet locations, camera pose estimation,
a differentiable homography module to map image into BEV coordinates, and
geometric reasoning to produce a BEV map of the people locations in the scene.
Experiments on complex crowded scenes demonstrate the power of the approach and
show superior performance over baselines derived from methods in the
literature. Applications of interest for public health decision makers are
finally discussed. Datasets, code and pretrained models are publicly available
at GitHub.
