Title: Real-time monitoring of social distancing with person marking and tracking system using YOLO V3 model

Authors: P. Pandiyan; Rajasekaran Thangaraj; M. Subramanian; R. Rahul; M. Nishanth; Indupriya Palanisamy

Addresses: Department of Electrical and Electronics Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India ' Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India ' Department of Science and Humanities, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India ' Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India ' Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India ' Kovan Labs, Coimbatore, Tamil Nadu, India

Abstract: The global economy has been affected enormously due to the spread of coronavirus (COVID-19). Even though, there is the availability of vaccines, social distancing in public places is one of the viable solutions to reduce the spreading of COVID-19 suggested by the World Health Organization (WHO) for fighting against the pandemic. This paper presents a YOLO v3 object detection model to automate the monitoring of social distancing among persons through a CCTV surveillance camera. Furthermore, this research work used to detect and track the person, measure the inter-person distance in the crowd under a challenging environment which includes partial visibility, lighting variations, and person occlusion. Moreover, the YOLO V3 model experiments with Darknet53 and ShuffleNetV2 backbone architecture. Compared with Darknet53 architecture, ShuffleNetV2 achieves better detection accuracy tested on Custom Video Footage Dataset (CVFD), Oxford Town Centre Dataset (OTCD), and Custom Personal Image Dataset (CPID) datasets.

Keywords: deep learning; COVID-19; social distancing; surveillance camera; crowd counting; computer vision.

DOI: 10.1504/IJSNET.2022.10045944

International Journal of Sensor Networks, 2022 Vol.38 No.3, pp.154 - 165

Received: 13 May 2021
Accepted: 14 May 2021

Published online: 24 Mar 2022 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article