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).
more »
« less
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%.
more »
« less
- 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
More Like this
-
-
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, Testbedsmore » « less
-
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
-
Social Distancing has proved a necessary measure in con- trolling the spread of Coronavirus. The CDC (Center for dis- ease control and prevention) in the United States recommends 6 feet as a safe distance between individuals. Therefore, a surveillance system capable of measuring distances between individuals can prove beneficial in limiting the spread. Video surveillance systems have already been introduced in various fields of our daily life to enhance security and protect individuals and sensitive infrastructure. In this work we present a way to use the existing video surveillance infrastructure to measure and monitor social distancing. We present a practical approach to monitor this distance using artificial intelligence and projective geometry techniques. Our approach, after initial setup, works in real-time requiring only a monocular surveillance camera feed. The proposed approach utilizes YOLO v4 neural network object detector for detecting pedestrians in the camera’s view. Projective transformation is used to localize the pedestrians on the ground. Finally, the real world distances between pedestrians is calculated and visualized with the right perspective and occlusion relations as if the distance marks are actually on the ground. All the implementation is in real-time, and is performed on python using the OPENCV libraries and the YOLO v4 neural net with pre- trained weights. Experimental results are provided to validate our approach. The code of this work will be made publicly available at GitHub upon acceptance.more » « less
-
This paper proposes the use of collaborative secondary data analysis (SDA) as a tool for building capacity in engineering education research. We first characterise the value of collaborative SDA as a tool to help emerging researchers develop skills in qualitative data analysis. We then describe an ongoing collaboration that involves a series of workshops as well as two pilot projects that seek to develop and test frameworks and practices for SDA in engineering education research. We identify emerging benefits and practical challenges associated with implementing SDA as a capacity building tool, and conclude with a discussion of future work.more » « less