A deep learning-based social distance monitoring framework for COVID-19. (February 2021)

• Record Type: Journal Article
• Authors: Ahmed, Imran; Ahmad, Misbah; Rodrigues, Joel J.P.C.; Jeon, Gwanggil; Din, Sadia
• Is Part Of: Sustainable cities and society. Volume 65(2021)
• Publication Date: 2021-02
• Subjects: Deep learning -- Social distancing -- COVID-19 -- Transfer learning -- Overhead view -- Person detection -- YOLOv3; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2020.102571
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: The purpose of this work is to provide a deep learning platform for social distance tracking. The framework uses the YOLOv3 object recognition paradigm to identify humans in video sequences. The transfer learning methodology is implemented to increase the accuracy of the model. The detection algorithm uses a pre-trained algorithm. To estimate social distance violations between people, we used an approximation of physical distance. Abstract: The ongoing COVID-19 corona virus outbreak has caused a global disaster with its deadly spreading. Due to the absence of effective remedial agents and the shortage of immunizations against the virus, population vulnerability increases. In the current situation, as there are no vaccines available; therefore, social distancing is thought to be an adequate precaution (norm) against the spread of the pandemic virus. The risks of virus spread can be minimized by avoiding physical contact among people. The purpose of this work is, therefore, to provide a deep learning platform for social distance tracking using an overhead perspective. The framework uses the YOLOv3 object recognition paradigm to identify humans in video sequences. The transfer learning methodology is also implemented to increase the accuracy of the model. In this way, the detection algorithm uses a pre-trained algorithm that is connected to an extra trained layer using an overhead human data set. The detection model identifies peoples using detected bounding box information. Using the Euclidean distance, the detected bounding box centroid's pairwise distances of people are determined. To estimate social distance violations between people, we used an approximation of physical distance to pixel and set a threshold. A violation threshold is established to evaluate whether or not the distance value breaches the minimum social distance threshold. In addition, a tracking algorithm is used to detect individuals in video sequences such that the person who violates/crosses the social distance threshold is also being tracked. Experiments are carried out on different video sequences to test the efficiency of the model. Findings indicate that the developed framework successfully distinguishes individuals who walk too near and breaches/violates social distances; also, the transfer learning approach boosts the overall efficiency of the model. The accuracy of 92% and 98% achieved by the detection model without and with transfer learning, respectively. The tracking accuracy of the model is 95%.