Computer Vision Based Social Distancing Detection


Authors : M. Murali; Deepak Yadav

Volume/Issue : Volume 7 - 2022, Issue 4 - April

Google Scholar : https://bit.ly/3IIfn9N

Scribd : https://bit.ly/36SSN19

DOI : https://doi.org/10.5281/zenodo.6499706

COVID-19 is a rapidly spreading viral disease which has challenged the health services of the world. Social distancing has been recommended as one of the best practice that helps to restrain the curve of COVID19 virus. The effective measure of Social distancing has helped to decrease the transmission rate of the infectious COVID-19 worldwide.Furthermore, the lack of temporalunderstanding among the people may cause unintentional breach of the social distancing norms. Hence, it is necessary to bring in a vision based concurrent flow that will spot the social distancing violations. Social Distancing limits the physical contact among the people and by doing such, the danger of spreading COVID-19 can be decreased. The main objective of this proposed systemis to create a deeplearning system to detect social distancing to recognize persons in video sequences. The proposed system will employ YOLOv3 object recognition algorithm. The significance of this model is improvised through the transfer learning process. The pre trained algorithm is coupled with the trained layer which uses an additional data that will help in the detection process. The Euclidean distance is used to compute the pairwise distances of objects from the identified bounding box centroid while the bounding box information helps to identify the objects.A social distancing violation threshold will beset to examinewhether the distance value among the people exceeds minimal barrier that has been set for social distance. This work will define a social density value and show that pedestrian-density is heldunder the value defined. Thus thechance of a Social Distancing violation could be prevented.

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