Convolutional neural networks for parking space detection in downfire urban radar

Journal article
(Review article)


Publication Details

Author(s): Martinez Garcia J, Zoeke D, Vossiek M
Journal: International Journal of Microwave and Wireless Technologies
Publisher: Cambridge University Press (CUP)
Publication year: 2018
Volume: 10
Journal issue: 5-6
Pages range: 643-650
ISSN: 1759-0787
Language: English


Abstract

We present a method for detecting parking spaces in radar images based
on convolutional neural networks (CNN). A multiple-input multiple-output
radar is used to render a slant-range image of the parking scenario and
a background estimation technique is applied to reduce the impact of
dynamic interference from the surroundings by separating the static
background from moving objects in the scene. A CNN architecture, that
also incorporates mechanisms to generalize the model to new scenarios,
is proposed to determine the occupancy of the parking spaces in the
static radar images. The experimental results show very high accuracy
even in scenarios where little or no training data is available, proving
the viability of the proposed approach for its implementation at large
scale with reduced deployment efforts.


FAU Authors / FAU Editors

Martinez Garcia, Javier
Lehrstuhl für Hochfrequenztechnik
Vossiek, Martin Prof. Dr.-Ing.
Lehrstuhl für Hochfrequenztechnik


How to cite

APA:
Martinez Garcia, J., Zoeke, D., & Vossiek, M. (2018). Convolutional neural networks for parking space detection in downfire urban radar. International Journal of Microwave and Wireless Technologies, 10(5-6), 643-650. https://dx.doi.org/10.1017/s1759078718000466

MLA:
Martinez Garcia, Javier, Dominik Zoeke, and Martin Vossiek. "Convolutional neural networks for parking space detection in downfire urban radar." International Journal of Microwave and Wireless Technologies 10.5-6 (2018): 643-650.

BibTeX: 

Last updated on 2019-29-01 at 16:23

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