Cristani, M., Bue, A. D., Murino, V., Setti, F. and Vinciarelli, A. (2020) The visual social distancing problem. IEEE Access, 8, pp. 126876-126886. (doi: 10.1109/ACCESS.2020.3008370)
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Abstract
One of the main and most effective measures to contain the recent viral outbreak is the maintenance of the so-called Social Distancing (SD). To comply with this constraint, governments are adopting restrictions over the minimum inter-personal distance between people. Given this actual scenario, it is crucial to massively measure the compliance to such physical constraint in our life, in order to figure out the reasons of the possible breaks of such distance limitations, and understand if this implies a potential threat. To this end, we introduce the Visual Social Distancing (VSD) problem, defined as the automatic estimation of the inter-personal distance from an image, and the characterization of related people aggregations. VSD is pivotal for a non-invasive analysis to whether people comply with the SD restriction, and to provide statistics about the level of safety of specific areas whenever this constraint is violated. We first point out that measuring VSD is not only a geometrical problem, but it also implies a deeper understanding of the social behaviour in the scene. The aim is to truly detect potentially dangerous situations while avoiding false alarms (e.g., a family with children or relatives, an elder with their caregivers), all of this by complying with current privacy policies. We then discuss how VSD relates with previous literature in Social Signal Processing and indicate a path to research new Computer Vision methods that can possibly provide a solution to such problem. We conclude with future challenges related to the effectiveness of VSD systems, ethical implications and future application scenarios.
| Item Type: | Articles |
|---|---|
| Additional Information: | This work was supported in part by the projects of the Italian Ministry of Education, United Kingdom Research and Innovation (MIUR), Dipartimenti di Eccellenza, from 2018 to 2022. The work of Alessandro Vinciarelli was supported in part by the United Kingdom Research and Innovation (UKRI) under Grant EP/S02266X/1, and in part by the Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/N035305/1. |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Vinciarelli, Professor Alessandro |
| Authors: | Cristani, M., Bue, A. D., Murino, V., Setti, F., and Vinciarelli, A. |
| College/School: | College of Science and Engineering > School of Computing Science |
| Research Group: | Social AI |
| Journal Name: | IEEE Access |
| Publisher: | IEEE |
| ISSN: | 2169-3536 |
| ISSN (Online): | 2169-3536 |
| Published Online: | 10 July 2020 |
| Copyright Holders: | Copyright © 2020 The Authors |
| First Published: | First published in IEEE Access 8:126876-126886 |
| Publisher Policy: | Reproduced under a Creative Commons License |
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