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Title	Smart Parking: Detecting Vacant Spots From An Angled Viewpoint
ID_Doc	51304
Authors	Sayson J.L.; Valdehueza A.; Maghanoy M.J.; Flores P.R.; Haim S.; Alagon F.J.; Visitacion S.; Villame C.M.; Clar S.; Zamayla A.; Aleluya E.R.
Year	2024
Published	Proceedings - 2024 IEEE Conference on Dependable, Autonomic and Secure Computing, DASC 2024
DOI	http://dx.doi.org/10.1109/DASC64200.2024.00019
Abstract	Finding available parking spots in urban areas is a challenging and time-consuming task due to inefficient traditional methods. This paper presents a smart parking solution that leverages You Only Look Once (YOLOv5) for car detection and body type classification, coupled with Simple Online Real-time Tracking (SORT) for continuous tracking of vehicle bounding boxes. This study introduces a straightforward 2D environment modeling to improve parking management. The proposed system demonstrates performance with a top precision of 0.992, a top recall of 1.0, a mean Average Precision (mAP) of 0.995 at IoU threshold 0.50, and a mAP of 0.953 across IoU thresholds 0.50-0.95. The color detection accuracy and parking occupancy detection also achieved a top score of 100% in the 30-day test experiment. Future work involves deploying the system in various sites to evaluate its robustness and effectiveness in diverse real-world conditions. © 2024 IEEE.
Author Keywords	computer vision; parking management; smart cities; urban computing; vehicle detection

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