Deep Learning Based Parking Vacancy Detection for Smart Cities Xu, Qiangwen Sun, Mengyao Fu, Bailing Zhao, Yijun 2021-12-24T18:30:40Z 2021-12-24T18:30:40Z 2022-01-04
dc.description.abstract Parking shortage is a major problem in modern cities. Drivers cruising in search of a parking space directly translate into frustration, traffic congestion, and excessive carbon emission. We introduce a simple and effective deep learning-based parking space notification (PSN) system to inform drivers of new parking availabilities and re-occupancy of the freed spaces. Our system is particularly designed to target areas with severe parking shortages (i.e., nearly all parking spaces are occupied), a situation that allows us to convert the problem of detecting parking vacancies into recognizing vehicles leaving from their stationary positions. Our PSN system capitalizes on a calibrated Mask R-CNN model and a unique adaptation of the IoU concept to track the changes of vehicle positions in a video stream. We evaluated PSN using videos from a CCTV camera installed at a private parking lot and publicly available YouTube videos. The PSN system successfully captured all new parking vacancies arising from leaving vehicles with no false positive detections. Prompt notification messages were sent to users via cloud messaging services.
dc.format.extent 7 pages
dc.identifier.doi 10.24251/HICSS.2022.928
dc.identifier.isbn 978-0-9981331-5-7
dc.language.iso eng
dc.relation.ispartof Proceedings of the 55th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.subject Smart (City) and Data Streaming Application Development: Challenges and Experiences
dc.subject deep learning
dc.subject iot
dc.subject object localization
dc.subject parking vacancy detection
dc.subject smart cities
dc.title Deep Learning Based Parking Vacancy Detection for Smart Cities
dc.type.dcmi text
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