Open Access

ARTICLE

A Real-Time Pedestrian Social Distancing Risk Alert System for COVID-19

Zhihan Liu¹, Xiang Li¹, Siqi Liu², Wei Li^1,*, Xiangxu Meng¹, Jing Jia³

1 College of Computer Science and Technology, Harbin Engineering University, Harbin, 150001, China
2 Department of Clinical Laboratory, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, 150081, China
3 Faculty of Arts, Design and Architecture, School of Built Environment, The University of New South Wales, Sydney, NSW 2052, Australia

* Corresponding Author: Wei Li. Email:

Computer Systems Science and Engineering 2023, 47(1), 937-954. https://doi.org/10.32604/csse.2023.039417

Received 28 January 2023; Accepted 11 April 2023; Issue published 26 May 2023

Abstract

The COVID-19 virus is usually spread by small droplets when talking, coughing and sneezing, so maintaining physical distance between people is necessary to slow the spread of the virus. The World Health Organization (WHO) recommends maintaining a social distance of at least six feet. In this paper, we developed a real-time pedestrian social distance risk alert system for COVID-19, which monitors the distance between people in real-time via video streaming and provides risk alerts to the person in charge, thus avoiding the problem of too close social distance between pedestrians in public places. We design a lightweight convolutional neural network architecture to detect the distance between people more accurately. In addition, due to the limitation of camera placement, the previous algorithm based on flat view is not applicable to the social distance calculation for cameras, so we designed and developed a perspective conversion module to reduce the image in the video to a bird's eye view, which can avoid the error caused by the elevation view and thus provide accurate risk indication to the user. We selected images containing only person labels in the COCO2017 dataset to train our network model. The experimental results show that our network model achieves 82.3% detection accuracy and performs significantly better than other mainstream network architectures in the three metrics of Recall, Precision and mAP, proving the effectiveness of our system and the efficiency of our technology.

---

Keywords

---