A Social Distance Estimation and Crowd Monitoring System for Surveillance Cameras

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 2; p. 418
• Main Authors: Al-Sa’d, Mohammad, Kiranyaz, Serkan, Ahmad, Iftikhar, Sundell, Christian, Vakkuri, Matti, Gabbouj, Moncef
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.01.2022; MDPI
• Subjects: Algorithms; Boxes; Cameras; Coronaviruses; COVID-19; crowd monitoring; Crowding; Disease transmission; Human subjects; Humans; Localization; Machine learning; Monitoring systems; person detection and tracking; Physical Distancing; pose estimation; Privacy; SARS-CoV-2; Sensors; Social distancing; Surveillance; video surveillance; Violations; Finland; COVID-19; crowd monitoring; person detection and tracking; video surveillance; social distancing; pose estimation
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22020418

Social distancing is crucial to restrain the spread of diseases such as COVID-19, but complete adherence to safety guidelines is not guaranteed. Monitoring social distancing through mass surveillance is paramount to develop appropriate mitigation plans and exit strategies. Nevertheless, it is a labor-intensive task that is prone to human error and tainted with plausible breaches of privacy. This paper presents a privacy-preserving adaptive social distance estimation and crowd monitoring solution for camera surveillance systems. We develop a novel person localization strategy through pose estimation, build a privacy-preserving adaptive smoothing and tracking model to mitigate occlusions and noisy/missing measurements, compute inter-personal distances in the real-world coordinates, detect social distance infractions, and identify overcrowded regions in a scene. Performance evaluation is carried out by testing the system’s ability in person detection, localization, density estimation, anomaly recognition, and high-risk areas identification. We compare the proposed system to the latest techniques and examine the performance gain delivered by the localization and smoothing/tracking algorithms. Experimental results indicate a considerable improvement, across different metrics, when utilizing the developed system. In addition, they show its potential and functionality for applications other than social distancing.