A Novel Approach to Detect Social Distancing Among People in College Campus



COVID-19, Social Distance, YOLO V3 Algorithm, SMS Alerts


The world has shrunk due to the COVID-19. This virus took many lives of people. Hence the government proposed a lockdown and closed the educational institutions to decrease the death rate. The cases are controlled to some extent, but still the spread of the virus is not yet controlled. Though lockdown has been released and the educational institutions are being slowly opened with some conditions like wearing masks, maintaining social distance in the public areas and avoiding contact with the infected persons. But the people are not maintaining social distance in the colleges and schools, which can cause risk. Hence it is mandatory to build a model which identifies the people who cross the safe limit of distance. This work focuses on determining the social distance in the educational institutions mainly in the colleges. Generally, most of the colleges have CCTV cameras fixed on the campus, in order to determine the social distance among the people, the videos are taken from those CCTV cameras. The people are detected from the video clips using the You Only Look Once Version 3 (YOLO V3) algorithm which is trained on the Common Objects in Context (COCO) dataset. And the safety distance among the people is found using Euclidean distance. The minimum threshold value is fixed to the safe distance suggested by World Health Organization (WHO). Moreover, a warning is sent to the corresponding coordinators through Short Message Service (SMS) when the people are not in the safer social distance.


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Y.C. Hou, M.Z. Baharuddin, S. Yussof and S. Dzulkifly, “Social distancing detection with deep learning model”, in: 8th International Conference on Information Technology and Multimedia (ICIMU), IEEE, 2020, pp. 334-338.

A.H. Ahamad, N. Zaini and M.F.A. Latip, “Person Detection for Social Distancing and Safety Violation Alert based on Segmented ROI”, in 10th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), IEEE, 2020, pp. 113-118.

K.. Suresh, S. Bhuvan and M.B. Palangappa “Social Distance Identification Using Opti-mized Faster Region-Based Convolutional Neural Network”, in: 5th International Conference on Computing Methodologies and Communication (ICCMC), IEEE, 2021, pp. 753-760.

S. Degadwala, D. Vyas, H. Dave and A. Mahajan, “Visual Social Distance Alert System Using Computer Vision & Deep Learning”, in 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), IEEE, 2020, pp. 1512-1516.

S. Saponara, A. Elhanashi and A. Gagliardi, “Implementing a real-time, AI-based, people detection and social distancing measuring system for Covid-19”, Journal of Real-Time Image Processing, pp.1-11, 2021.

I. Chakraborty and P. Maity, “COVID-19 outbreak: Migration, effects on society, global environment and prevention”, Science of the Total Environment, 728, p.138882, 2020.

S.V. Shetty, K.. Anand, S. Pooja, K.A. Punnya and M. Priyanka, “Social Distancing and Face Mask Detection using Deep Learning Models: A Survey”, in Asian Conference on Innovation in Technology (ASIANCON), IEEE, 2021, pp. 1-6.

M. Qian and J. Jiang, “COVID-19 and social distancing”, Journal of Public Health, pp.1-3, 2020.


S. Srivastava, A.V. Divekar, C. Anilkumar, I. Naik, V. Kulkarni and V. Pattabiraman, “Comparative analysis of deep learning image detection algorithms”, Journal of Big Data, Vol. 8, No.1, pp.1-27, 2021

Overall flow of proposed work




How to Cite

Hermina, J. ., Karpagam, N. S. ., Deepika, P. ., Jeslet, D. S. ., & Komarasamy, D. (2022). A Novel Approach to Detect Social Distancing Among People in College Campus. International Journal of Intelligent Systems and Applications in Engineering, 10(2), 153–158. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/1823



Research Article