A Proposal to Ensure Social Distancing with Deep Learning-based Object Detection
Francesco Mercaldo, Fabio Martinelli, Antonella Santone
Abstract
Social distancing is becoming really important in last month as a vehicle to limit the COVID-19 Coronavirus pandemic. Generally speaking, it is effective to control the spread of contagious diseases. In this context there is the need to monitor social distancing violations: for this reason in this paper we propose a social distancing detector able to count the violations by analysing video streams. Preliminary results show that the proposed method can be employed to guarantee social distancing. Moreover we discuss several suggestions aimed to improve the following proposal.
Topics & Concepts
Social distanceComputer scienceContext (archaeology)DistancingCoronavirus disease 2019 (COVID-19)Computer securityPandemicInternet privacyArtificial intelligenceMedicineGeographyInfectious disease (medical specialty)ArchaeologyDiseasePathologyCOVID-19 diagnosis using AIVideo Surveillance and Tracking MethodsAnomaly Detection Techniques and Applications