A Social Distance Estimation and Crowd Monitoring System for Surveillance Cameras
التاريخ
2022المؤلف
Al-Sa'd M.Kiranyaz, Mustafa Serkan
Ahmad I.
Sundell C.
Vakkuri M.
Gabbouj M.
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البيانات الوصفية
عرض كامل للتسجيلةالملخص
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.
معرّف المصادر الموحد
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122181933&doi=10.3390%2fs22020418&partnerID=40&md5=a88ac281c29cbd852d55c67575d56bdeالمجموعات
- الهندسة الكهربائية [2611 items ]