Abstract
Social distancing (SD) is an effective measure to prevent the spread of the infectious Coronavirus Disease 2019 (COVID-19). However, a lack of spatial awareness may cause unintentional violations of this new measure. Against this backdrop, we propose an active surveillance system to slow the spread of COVID-19 by warning individuals in a region-of-interest. Our contribution is twofold. First, we introduce a vision-based real-time system that can detect SD violations and send non-intrusive audio-visual cues using state-of-the-art deep-learning models. Second, we define a novel critical social density value and show that the chance of SD violation occurrence can be held near zero if the pedestrian density is kept under this value. The proposed system is also ethically fair: it does not record data nor target individuals, and no human supervisor is present during the operation. The proposed system was evaluated across real-world datasets.
Highlights
Distancing and Critical DensityWith the outbreak of the novel Coronavirus Disease 2019 (COVID-19) [1], social distancing (SD) emerged as an effective measure against it
We propose a non-intrusive, artificial intelligence (AI)-based active surveillance system for social distancing detection, monitoring, analysis, and control
To further reduce the probability of social distancing violation occurrence, instead of using ρ|v=0, we propose to determine the critical social density as: pred ρc = ρlb, (7)
Summary
With the outbreak of the novel Coronavirus Disease 2019 (COVID-19) [1], social distancing (SD) emerged as an effective measure against it. Maintaining social distancing in public areas such as transit stations, shopping malls, and university campuses is crucial to prevent or slow the spread of the virus. The practice of social distancing (SD) may continue in the following years until the spread of the virus is completely phased out. Social distancing is prone to be violated unwillingly, as populations are not accustomed to keeping the necessary 2-meter bubble around each individual. This work proposes a visionbased automatic warning system that can detect social distancing statuses and identify a critical pedestrian density threshold to modulate inflow to crowded areas. Besides being an automated monitoring and warning system, the proposed framework can serve as a tool to detect key variables and statistics for local and global virus control
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
