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Smart city article details

Title	Parking Lots Management And Visualization In The Smart City - Digital Twin Context
ID_Doc	41312
Authors	Shrivastav C.S.; Masola A.; Capodieci N.; Cavicchioli R.
Year	2023
Published	Proceedings - 2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023
DOI	http://dx.doi.org/10.1109/MetaCom57706.2023.00045
Abstract	In recent years, extensive research has been conducted to develop traffic surveillance and monitoring applications using advanced technologies in a Smart-City context. One of the fundamental challenges in an urban city scenario is developing an efficient yet affordable parking lot management system. Such systems play a crucial role in providing the necessary data to local municipalities to make informed decisions to better manage the traffic flow while saving valuable resources and time. In this paper, we propose a novel end-to-end system architecture for a real-time video-based parking management system that can be easily scaled and enforced in a Smart-City context. Using state-of-the-art object detection and tracking methods combined with novel Static Object Detection techniques, we demonstrate a localized strategy for identifying potential parking spaces. A separate parking occupancy classification pipeline is discussed which provides fast and accurate availability status of these parking spaces. Furthermore, a 3D digital-twin application to monitor and visualize the parking spaces is demonstrated. Our experiments in a real-world scenario demonstrate the significant potential of our proposed architecture to easily scale to a given city with minimal manual efforts and lower implementation costs. © 2023 IEEE.
Author Keywords	5G network; Classification; Deep Learning; Digital Twin; Parking Detection; Smart City

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