RT Journal Article
T1 On-street parking space localization with deep learning using low-quality images from public cameras
A1 Morell, José Ángel
A1 Luque-Polo, Gabriel Jesús
A1 Alba-Torres, Enrique
K1 Movilidad residencial
K1 Ingeniería sostenible
K1 Visión artificial
AB The increasing demand for new services in cities leads to challenges that need an intelligent, holistic approach (smart cities). A crucial aspect of smart city planning is effectively managing public on-street parking spaces, influencing overall urban mobility and environmental sustainability. However, detecting these spaces is complex, often requiring costly cameras or sensors, and the variability in parking space sizes, depending on the vehicles parked, adds to the difficulty. Existing city cameras for traffic monitoring could be a solution, but their lowimage quality and frequent movements (taking images from different angles) make accurate detection challenging. We propose a novel method for locating on-street parking spaces using low-quality images from non-static public traffic cameras. This approach is dataset-independent, applicable to various cities, and employs deep-learning models pretrained for tasks like vehicle detection, repurposing them for the novel task of identifying on-street public parking spaces. This method avoids specific retraining and intensive manual labeling. Tested in Malaga, Spain,the pipeline includes Extraction (sourcing images from Internet traffic cameras), Matching (recognizing common features between reference and new images for detecting camera movements), Preprocessing (comparing different denoising and image-enhancing techniques for improving model inference), Detection (using models like YOLOv8 and Detectron2 for vehicle detection), and Postprocessing (transforming perspectives to estimate real-world parking space coordinates and sizes). Experimental results demonstrate that our proposal achieves accurate parking spacedetection even in extreme light conditions and camera movements, providing a valuable new tool for parking management and urban planning.
PB Elsevier
YR 2025
FD 2025-05-08
LK https://hdl.handle.net/10630/38611
UL https://hdl.handle.net/10630/38611
LA eng
NO José Ángel Morell, Gabriel Luque, Enrique Alba, On-street parking space localization with deep learning using low-quality images from public cameras, Internet of Things, Volume 32, 2025, 101619, ISSN 2542-6605, https://doi.org/10.1016/j.iot.2025.101619.
NO his research is funded by PID 2020-116727RB-I00 (HUmove) funded by MCIN/AEI/10.13039/501100011033; and TAILOR ICT-48 Network (No 952215) funded by EU Horizon 2020 research and innovation programme. Funding for open access charge: Universidad de Málaga / CBUA. The authors thank the Supercomputing and Bioinnovation Center (SCBI) for their provision of computational resources and technical support. The views expressed are purely those of the writer and may not in any circumstances be regarded as stating an official position of the European Commission.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026