Holistic interpretation of public scenes using computer vision and temporal graphs to identify social distancing violations

Jayatilaka, Gihan, Hassan, Jameel, Sritharan, Suren, Senanayaka, Janith Bandara, Weligampola, Harshana, Godaliyadda, Roshan, Ekanayake, Parakrama ORCID: https://orcid.org/0000-0003-0362-3767, Herath, Vijitha, Ekanayake, Janaka ORCID: https://orcid.org/0000-0003-0362-3767 and Dharmaratne, Samath 2022. Holistic interpretation of public scenes using computer vision and temporal graphs to identify social distancing violations. Applied Sciences 12 (17) , e8428. 10.3390/app12178428

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Abstract

Social distancing measures are proposed as the primary strategy to curb the spread of the COVID-19 pandemic. Therefore, identifying situations where these protocols are violated has implications for curtailing the spread of the disease and promoting a sustainable lifestyle. This paper proposes a novel computer vision-based system to analyze CCTV footage to provide a threat level assessment of COVID-19 spread. The system strives to holistically interpret the information in CCTV footage spanning multiple frames to recognize instances of various violations of social distancing protocols, across time and space, as well as identification of group behaviors. This functionality is achieved primarily by utilizing a temporal graph-based structure to represent the information of the CCTV footage and a strategy to holistically interpret the graph and quantify the threat level of the given scene. The individual components are evaluated in a range of scenarios, and the complete system is tested against human expert opinion. The results reflect the dependence of the threat level on people, their physical proximity, interactions, protective clothing, and group dynamics, with a system performance of 76% accuracy.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Additional Information:	License information from Publisher: LICENSE 1: URL: https://creativecommons.org/licenses/by/4.0/
Publisher:	MDPI
Date of First Compliant Deposit:	26 August 2022
Date of Acceptance:	15 August 2022
Last Modified:	04 May 2023 22:48
URI:	https://orca.cardiff.ac.uk/id/eprint/152159

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