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Title	Embedded Parking Slot Detection And Classification For Smart City Scenarios
ID_Doc	22688
Authors	Lungoci T.; Negru M.
Year	2024
Published	Proceedings - 2024 IEEE 20th International Conference on Intelligent Computer Communication and Processing Conference, ICCP 2024
DOI	http://dx.doi.org/10.1109/ICCP63557.2024.10793025
Abstract	As cities are becoming more crowded due to the population moving to urban areas, smart parking and management are essential elements of the transportation system. There are efforts in developing vision-based automated parking systems by detecting the ground markings accurately in real-time from multiple surround-view cameras. We are not interested in automated parking, but in collecting relevant parking occupancy statistics from the city streets. In this paper, we propose a novel method based on a single front-view camera and a convolutional neural network (CNN) for detecting the parking slots and classifying them based on occupancy. This information collected by the camera is sent to a central processing cloud and can serve as a basis for many Smart City parking management applications, by providing useful dynamic occupancy statistics. © 2024 IEEE.
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