Monitoring Social-distance in Wide Areas during Pandemics: a Density Map and Segmentation Approach

TL;DR

This paper introduces a novel deep learning framework for monitoring social distancing in large, crowded areas during pandemics, effectively handling occlusions and wide views to identify violations.

Contribution

It presents a new end-to-end deep learning approach using density maps and segmentation for social distancing monitoring in wide, occluded environments, with a new ground truth creation method.

Findings

Effective detection of social distancing violations in wide areas

Robust performance under occlusions and distance

Two approaches evaluated: density-map-based and segmentation-based

Abstract

With the relaxation of the containment measurements around the globe, monitoring the social distancing in crowded public places is of grate importance to prevent a new massive wave of COVID-19 infections. Recent works in that matter have limited themselves by detecting social distancing in corridors up to small crowds by detecting each person individually considering the full body in the image. In this work, we propose a new framework for monitoring the social-distance using end-to-end Deep Learning, to detect crowds violating the social-distance in wide areas where important occlusions may be present. Our framework consists in the creation of a new ground truth based on the ground truth density maps and the proposal of two different solutions, a density-map-based and a segmentation-based, to detect the crowds violating the social-distance constrain. We assess the results of both approaches by using the generated ground truth from the PET2009 and CityStreet datasets. We show that our framework performs well at providing the zones where people are not following the social-distance even when heavily occluded or far away from one camera.