The COVID-19 outbreak has affected millions of people across the globe and is continuing to spread at a drastic scale. Out of the numerous steps taken to control the spread of the virus, social distancing has been a crucial and effective practice. However, recent reports of social distancing violations suggest the need for non-intrusive detection techniques to ensure safety in public spaces. In this paper, a real-time detection model is proposed to identify handshake interactions in a range of realistic scenarios with multiple people in the scene and also detect multiple interactions in a single frame. This is the first work that performs dyadic interaction localization in a multi-person setting. The efficacy of the proposed model was evaluated across two different datasets on more than 3200 frames, thus enabling a robust localization model in different environments. The proposed model is the first dyadic interaction localizer in a multi-person setting, which enables it to be used in public spaces to identify handshake interactions and thereby identify and mitigate COVID-19 transmission.
@article{arxiv.2110.09571,
title = {Hands Off: A Handshake Interaction Detection and Localization Model for COVID-19 Threat Control},
author = {A. S. Jameel Hassan and Suren Sritharan and Gihan Jayatilaka and Roshan I. Godaliyadda and Parakrama B. Ekanayake and Vijitha Herath and Janaka B. Ekanayake},
journal= {arXiv preprint arXiv:2110.09571},
year = {2021}
}