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• W3190673744 abstract "Random outbreaks of infectious diseases in the past have left a persistent impact on societies. Currently, COVID-19 is spreading worldwide and consequently risking human lives. In this regard, maintaining physical distance has turned into an essential precautionary measure to curb the spread of the virus. In this paper, we propose an autonomous monitoring system that is able to enforce physical distancing rules in large areas round the clock without human intervention. We present a novel system to automatically detect groups of individuals who do not comply with physical distancing constraints, i.e., maintaining a distance of 1 m, by tracking them within large areas to re-identify them in case of repetitive non-compliance and enforcing physical distancing. We used a distributed network of multiple CCTV cameras mounted to the walls of buildings for the detection, tracking and re-identification of non-compliant groups. Furthermore, we used multiple self-docking autonomous robots with collision-free navigation to enforce physical distancing constraints by sending alert messages to those persons who are not adhering to physical distancing constraints. We conducted 28 experiments that included 15 participants in different scenarios to evaluate and highlight the performance and significance of the present system. The presented system is capable of re-identifying repetitive violations of physical distancing constraints by a non-compliant group, with high accuracy in terms of detection, tracking and localization through a set of coordinated CCTV cameras. Autonomous robots in the present system are capable of attending to non-compliant groups in multiple regions of a large area and encouraging them to comply with the constraints." @default.
• W3190673744 created "2021-08-16" @default.
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• W3190673744 date "2021-08-04" @default.
• W3190673744 modified "2023-10-18" @default.
• W3190673744 title "Multi-Agent Robot System to Monitor and Enforce Physical Distancing Constraints in Large Areas to Combat COVID-19 and Future Pandemics" @default.
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• W3190673744 doi "https://doi.org/10.3390/app11167200" @default.
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