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The COVID-19 pandemic has caused an unprecedented global public healthcrisis. Given its inherent nature, social distancing measures are proposed asthe primary strategies to curb the spread of this pandemic. Therefore,identifying situations where these protocols are violated, has implications forcurtailing the spread of the disease and promoting a sustainable lifestyle.This paper proposes a novel computer vision-based system to analyze CCTVfootage to provide a threat level assessment of COVID-19 spread. The systemstrives to holistically capture and interpret the information content of CCTVfootage spanning multiple frames to recognize instances of various violationsof social distancing protocols, across time and space, as well asidentification of group behaviors. This functionality is achieved primarily byutilizing a temporal graph-based structure to represent the information of theCCTV footage and a strategy to holistically interpret the graph and quantifythe threat level of the given scene. The individual components are tested andvalidated on a range of scenarios and the complete system is tested againsthuman expert opinion. The results reflect the dependence of the threat level onpeople, their physical proximity, interactions, protective clothing, and groupdynamics. The system performance has an accuracy of 76%, thus enabling adeployable threat monitoring system in cities, to permit normalcy andsustainability in the society.

Holistic Interpretation of Public Scenes Using Computer Vision and Temporal Graphs to Identify Social Distancing Violations