Live Social Spacing Tracker Based on Domain Detection - Computational Intelligence in Data Science
Conference Papers Year : 2022

Live Social Spacing Tracker Based on Domain Detection

Divyank Agarwal
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Monark Mehta
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Nirmala Paramanandham
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Abstract

Corona virus Disease-2019 (COVID-19) is caused by infection with the severe acute respiratory syndrome (SARS) coronavirus. In this COVID 19 pandemic, the virus spread bringing the whole world to downfall. This disease can spread through the slightest touch, breathing the same air or using same basic things like clothes, hairs, combs, etc. These viruses can live for hours even without a host body. To prevent the spread, the world was put on lockdown, and people were constrained to their homes, but human life cannot go on without interaction. We need a better way of preventing the spread of this type of disease. Hence a detector is proposed for measuring the social distance between the people. This social distancing detector can track the people who are not following social distancing norms, then that person can be tracked down or the person is marked in red and triggers the warning. In this paper we will be using Computer Neural Network (CNN) to process our images and videos because CNN is a type of artificial neural network that is used in recognition of large pixel videos or images. The proposed technique can be the perfect way to help the person while people carry out their daily tasks. From the evaluation parameters it is proven that the proposed technique yields better results when compared to the state- of- the art techniques.
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Dates and versions

hal-04381301 , version 1 (09-01-2024)

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Divyank Agarwal, Monark Mehta, Nirmala Paramanandham. Live Social Spacing Tracker Based on Domain Detection. 5th International Conference on Computational Intelligence in Data Science (ICCIDS), Mar 2022, Virtual, India. pp.115-123, ⟨10.1007/978-3-031-16364-7_9⟩. ⟨hal-04381301⟩
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