Birds Eye View Social Distancing Analysis System

• URL: http://arxiv.org/abs/2112.07159v1
• Date: Tue, 14 Dec 2021 04:47:12 GMT
• Title: Birds Eye View Social Distancing Analysis System
• Authors: Zhengye Yang, Mingfei Sun, Hongzhe Ye, Zihao Xiong, Gil Zussman, Zoran Kostic
• Abstract summary: 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. We propose and evaluate a privacy-preserving social distancing analysis system (B-SDA), which uses bird's-eye view video recordings of pedestrians who cross traffic intersections.
• Score: 9.349085511919046
• License: http://creativecommons.org/licenses/by/4.0/
• 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. We propose and evaluate a 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. B-SDA is used to compare pedestrian behavior based on pre-pandemic and pandemic videos in a major metropolitan area. The accomplished pedestrian detection performance is $63.0\%$ $AP_{50}$ and the tracking performance is $47.6\%$ MOTA. The social distancing violation rate of $15.6\%$ during the pandemic is notably lower than $31.4\%$ pre-pandemic baseline, indicating that pedestrians followed CDC-prescribed social distancing recommendations. The proposed system is suitable for deployment in real-world applications.

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