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Earth Science Informatics

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11-01-2022 | Research Article

Real time deep learning framework to monitor social distancing using improved single shot detector based on overhead position

Authors: Bharathi Gopal, Anandharaj Ganesan

Published in: Earth Science Informatics | Issue 1/2022

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Abstract

The current COVID 19 halo infection has caused a severe catastrophe with its deadly spread. Despite the implementation of the vaccine, the severity of the infection has not diminished, and it has become stronger and more destructive. So, the only solution to protect ourselves from infection is social-distancing. Although social-distancing has been in practice for a long time, in most places it is not effectively followed, and it is very difficult to find out manually at all times whether people are following it or not. Therefore, we introduced a newly developed framework of deep-learning technique to automatically identify whether people maintain social-distancing or not using remote sensing top view images. Initially, we are detecting the context of image which includes information about the environment. Our detection model recognizes individuals using the boundary box. Then centroid is determined over every detected boundary box. By means of applying Euclidean distance, the pair range distances of the detected boundary box centroid are determined. To evaluate whether the distance measurement exceeds the minimum social distance limit, the violation threshold is established. We used Improved Single Shot Detector model for detecting a person over an image. Experiments are carried out on widely collected remote sensing images from various environments. Based on the object detection algorithm of deep learning, a variety of performance metrics are compared to evaluate the efficiency of the proposed model. Research outcome shows that, our proposed model outperforms well while recognize and detect a person in a well excellent way.

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Title: Real time deep learning framework to monitor social distancing using improved single shot detector based on overhead position
Authors: Bharathi Gopal
Anandharaj Ganesan
Publication date: 11-01-2022
Publisher: Springer Berlin Heidelberg
Published in: Earth Science Informatics / Issue 1/2022
Print ISSN: 1865-0473
Electronic ISSN: 1865-0481
DOI: https://doi.org/10.1007/s12145-021-00758-4

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