Indoor Distance Measurement System COPS (COVID-19 Prevention System)

Minchan Shin and Nammee Moon
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Minchan Shin: Department of Computer Science and Engineering, Hoseo University, Asan 31499, Korea
Nammee Moon: Department of Computer Science and Engineering, Hoseo University, Asan 31499, Korea

Sustainability, 2021, vol. 13, issue 9, 1-13

Abstract: With the rapid spread of coronavirus disease 2019 (COVID-19), measures are needed to detect social distancing and prevent further infection. In this paper, we propose a system that detects social distancing in indoor environments and identifies the movement path and contact objects according to the presence or absence of an infected person. This system detects objects through frames of video data collected from a closed-circuit television using You Only Look Once (v. 4) and assigns and tracks object IDs using DeepSORT, a multiple object tracking algorithm. Next, the coordinates of the detected object are transformed by image warping the area designated by the top angle composition in the original frame. The converted coordinates are matched with the actual map to measure the distance between objects and detect the social distance. If an infected person is present, the object that violates the movement path and social distancing of the infected person is detected using the ID assigned to each object. The proposed system can be used to prevent the rapid spread of infection by detecting social distancing and detecting and tracking objects according to the presence of infected persons.

Keywords: COVID-19; social distancing; indoor environment; YOLOv4; multiple object tracking (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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