Bird's-eye View Social Distancing Analysis System
2022 Ieee International Conference on Communications Workshops (Icc Workshops)
; : 427-432, 2022.
Article
in English
| Web of Science | ID: covidwho-2042753
ABSTRACT
Social distancing can reduce the infection rates in respiratory pandemics such as COVID-19. Traffic intersections are particularly suitable for monitoring and evaluation of social distancing behavior in metropolises. Hence, in this paper, we propose and evaluate a real-time privacy-preserving social distancing analysis system (B-SDA), which uses bird's-eye view video recordings of pedestrians who cross traffic intersections. We devise algorithms for video pre-processing, object detection, and tracking which are rooted in the known computer-vision and deep learning techniques, but modified to address the problem of detecting very small objects/pedestrians captured by a highly elevated camera. We propose a method for incorporating pedestrian grouping for detection of social distancing violations, which achieves 0.92 F1 score. B-SDA is used to compare pedestrian behavior in pre-pandemic and during-pandemic videos in uptown Manhattan, showing that the social distancing violation rate of 15.6% during the pandemic is notably lower than 31.4% prepandemic baseline.
Full text:
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Collection:
Databases of international organizations
Database:
Web of Science
Language:
English
Journal:
2022 Ieee International Conference on Communications Workshops (Icc Workshops)
Year:
2022
Document Type:
Article
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