| Title |
Bird's-eye View Social Distancing Analysis System |
| ID_Doc |
43196 |
| Authors |
Yang, ZY; Sun, MF; Ye, HZ; Xiong, ZH; Zussman, G; Kostic, Z |
| Title |
Bird's-eye View Social Distancing Analysis System |
| Year |
2022 |
| Published |
|
| 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. |
| PDF |
https://arxiv.org/pdf/2112.07159
|
