  The COVID-19 pandemic has caused an unprecedented global public health
crisis. Given its inherent nature, social distancing measures are proposed as
the primary strategies to curb the spread of this pandemic. Therefore,
identifying situations where these protocols are violated, has implications for
curtailing the spread of the disease and promoting a sustainable lifestyle.
This paper proposes a novel computer vision-based system to analyze CCTV
footage to provide a threat level assessment of COVID-19 spread. The system
strives to holistically capture and interpret the information content of CCTV
footage spanning multiple frames to recognize instances of various violations
of social distancing protocols, across time and space, as well as
identification of group behaviors. This functionality is achieved primarily by
utilizing a temporal graph-based structure to represent the information of the
CCTV footage and a strategy to holistically interpret the graph and quantify
the threat level of the given scene. The individual components are tested and
validated on a range of scenarios and the complete system is tested against
human expert opinion. The results reflect the dependence of the threat level on
people, their physical proximity, interactions, protective clothing, and group
dynamics. The system performance has an accuracy of 76%, thus enabling a
deployable threat monitoring system in cities, to permit normalcy and
sustainability in the society.
