Abstract
In this paper we propose a vision-based two-stage parking detection module. The first stage detects vehicles in images based on a deep neural network. Then, a rule-based system determines the car parking spaces in the image. Experimental results show that our proposed algorithm detects parking space but it also obtains a high false positive rate. We plan to combine visual information with other information fonts to face this drawback.
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Acknowledgements
This research has been supported by the Spanish Government research funding RTI2018-098743-B-I00 (MICINN/FEDER) and the Comunidad de Madrid research funding grant Y2018/EMT-5062.
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De Luelmo, S.P., Del Viejo, E.G., Montemayor, A.S., Pantrigo, J.J. (2022). Visual Parking Space Estimation Using Detection Networks and Rule-Based Systems. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Adeli, H. (eds) Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence. IWINAC 2022. Lecture Notes in Computer Science, vol 13259. Springer, Cham. https://doi.org/10.1007/978-3-031-06527-9_58
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