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.
Keywords—Social distancing, Object detection, Smart city,
Testbeds
more »
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Bird’s-eye view social distancing analysis system
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.
more »
« less
- Award ID(s):
- 2038984
- NSF-PAR ID:
- 10355936
- Date Published:
- Journal Name:
- IEEE ICC 2022 Workshop on Edge Learning for 5G Mobile Networks and Beyond
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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