Social distancing is an effective public health tool to
reduce the spread of respiratory pandemics such as COVID-19.
To analyze compliance with social distancing policies, we design
two video-based pipelines for social distancing analysis, namely,
Auto-SDA and B-SDA. Auto-SDA (Automated video-based Social
Distancing Analyzer) is designed to measure social distancing
using street-level cameras. To avoid privacy concerns of using
street-level cameras, we further develop B-SDA (Bird’s eye view
Social Distancing Analyzer), which uses bird’s eye view cameras,
thereby preserving pedestrian’s privacy. We used the COSMOS
testbed deployed in West Harlem, New York City, to evaluate
both pipelines. In particular, Auto-SDA and B-SDA are applied
on videos recorded by two of COSMOS cameras deployed on the
2nd floor (street-level) and 12th floor (bird’s eye view) of Columbia
University’s Mudd building, looking at 120th St. and Amsterdam
Ave. intersection, New York City. Videos are recorded before and
during the peak of the pandemic, as well as after the vaccines
became broadly available. The results represent the impact of
social distancing policies on pedestrians’ social behavior. For
example, the analysis shows that after the lockdown, less than
55% of the pedestrians failed to adhere to the social distancing
policies, whereas this percentage increased to 65% after the
vaccines’ availability. Moreover, after the lockdown, 0-20% of
the pedestrians were affiliated with a social group, compared to
10-45% once the vaccines became available. The results also show
that the percentage of face-to-face failures has decreased from
42.3% (pre-pandemic) to 20.7%(after the lockdown).
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Auto-SDA: Automated video-based social distancing analyzer
Social distancing can reduce infection rates in respiratory pandemics such as COVID-19, especially in dense urban areas. To assess
pedestrians’ compliance with social distancing policies, we use the
pilot site of the PAWR COSMOS wireless edge-cloud testbed in
New York City to design and evaluate an Automated video-based
Social Distancing Analyzer (Auto-SDA) pipeline. Auto-SDA derives pedestrians’ trajectories and measures the duration of close
proximity events. It relies on an object detector and a tracker, however, to achieve highly accurate social distancing analysis, we design
and incorporate 3 modules into Auto-SDA: (i) a calibration module
that converts 2D pixel distances to 3D on-ground distances with less
than 10 cm error, (ii) a correction module that identifies pedestrians
who were missed or assigned duplicate IDs by the object detectiontracker and rectifies their IDs, and (iii) a group detection module that
identifies affiliated pedestrians (i.e., pedestrians who walk together
as a social group) and excludes them from the social distancing
violation analysis. We applied Auto-SDA to videos recorded at the
COSMOS pilot site before the pandemic, soon after the lockdown,
and after the vaccines became broadly available, and analyzed the
impacts of the social distancing protocols on pedestrians’ behaviors
and their evolution. For example, the analysis shows that after the
lockdown, less than 55% of the pedestrians violated the social distancing protocols, whereas this percentage increased to 65% after
the vaccines became available. Moreover, after the lockdown, 0-20%
of the pedestrians were affiliated with a social group, compared
to 10-45% once the vaccines became available. Finally, following
the lockdown, the density of the pedestrians at the intersection
decreased by almost 50%.
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- NSF-PAR ID:
- 10309929
- Date Published:
- Journal Name:
- in Proc. 3rd Workshop on Hot Topics in Video Analytics and Intelligent Edges (HotEdgeVideo’21)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3477083.3480154
