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Coronavirus has been spreading around the world since the end of 2019. Thevirus can cause acute respiratory syndrome, which can be lethal, and is easilytransmitted between hosts. Most states have issued state-at-home executiveorders, however, parks and other public open spaces have largely remained openand are seeing sharp increases in public use. Therefore, in order to ensurepublic safety, it is imperative for patrons of public open spaces to practicesafe hygiene and take preventative measures. This work provides a scalablesensing approach to detect physical activities within public open spaces andmonitor adherence to social distancing guidelines suggested by the US Centersfor Disease Control and Prevention (CDC). A deep learning-based computer visionsensing framework is designed to investigate the careful and proper utilizationof parks and park facilities with hard surfaces (e.g. benches, fence poles, andtrash cans) using video feeds from a pre-installed surveillance camera network.The sensing framework consists of a CNN-based object detector, a multi-targettracker, a mapping module, and a group reasoning module. The experiments arecarried out during the COVID-19 pandemic between March 2020 and May 2020 acrossseveral key locations at the Detroit Riverfront Parks in Detroit, Michigan. Thesensing framework is validated by comparing automatic sensing results withmanually labeled ground-truth results. The proposed approach significantlyimproves the efficiency of providing spatial and temporal statistics of usersin public open spaces by creating straightforward data visualizations forfederal and state agencies. The results can also provide on-time triggeringinformation for an alarming or actuator system which can later be added tointervene inappropriate behavior during this pandemic.

An Autonomous Approach to Measure Social Distances and Hygienic Practices during COVID-19 Pandemic in Public Open Spaces