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

TL;DR

This paper presents a scalable computer vision framework using deep learning to monitor social distancing and hygienic practices in public parks during COVID-19, aiding safety enforcement.

Contribution

It introduces a novel CNN-based sensing system for real-time monitoring of social distancing and hygiene adherence in public open spaces during the pandemic.

Findings

Framework accurately detects and tracks individuals in parks.

Significantly improves efficiency of spatial-temporal data collection.

Provides real-time visualizations and alerts for safety compliance.

Abstract

Coronavirus has been spreading around the world since the end of 2019. The virus can cause acute respiratory syndrome, which can be lethal, and is easily transmitted between hosts. Most states have issued state-at-home executive orders, however, parks and other public open spaces have largely remained open and are seeing sharp increases in public use. Therefore, in order to ensure public safety, it is imperative for patrons of public open spaces to practice safe hygiene and take preventative measures. This work provides a scalable sensing approach to detect physical activities within public open spaces and monitor adherence to social distancing guidelines suggested by the US Centers for Disease Control and Prevention (CDC). A deep learning-based computer vision sensing framework is designed to investigate the careful and proper utilization of parks and park facilities with hard surfaces (e.g. benches, fence poles, and trash cans) using video feeds from a pre-installed surveillance camera network. The sensing framework consists of a CNN-based object detector, a multi-target tracker, a mapping module, and a group reasoning module. The experiments are carried out during the COVID-19 pandemic between March 2020 and May 2020 across several key locations at the Detroit Riverfront Parks in Detroit, Michigan. The sensing framework is validated by comparing automatic sensing results with manually labeled ground-truth results. The proposed approach significantly improves the efficiency of providing spatial and temporal statistics of users in public open spaces by creating straightforward data visualizations for federal and state agencies. The results can also provide on-time triggering information for an alarming or actuator system which can later be added to intervene inappropriate behavior during this pandemic.