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Yet another Social Distancing Implementation for the COVID world

Author(s): Ashutosh Kothiwala, Aravind Chandramouli

Abstract

Effect of COVID-19 pandemic is widespread across the globe and social distancing is a highly effective preventive measure to minimize virus spread. In this paper, we propose a computer vision-based system that can be used to automatically monitor social distancing compliance for single/multiple surveillance cameras in different environment settings like office, warehouse etc. Our system implementation follows a two-step process – (1) human detection and (2) check for social distancing. For human detection, we use faster RCNN object detector whereas, for social distancing compliance we calculate Euclidian distance between humans in bird eye view plane.

Considering the nature of problem, we use high Recall object detector to minimize undetected humans and balance its natural corollary – i.e. low Precision, with a wrapper that filters out false positives (non-human objects). Our overall Precision is improved further by doing camera calibration and filtering short non-compliance events (t < 2 s). Finally, we discuss different deployment options for the tool to be effective.

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