Parking Space Occupancy Verification - Improving Robustness using a Convolutional Neural Network

Troels H. P. Jensen; Helge T. Schmidt; Niels D. Bodin; Kamal Nasrollahi; Thomas B. Moeslund

doi:10.5220/0006135103110318

Parking Space Occupancy Verification - Improving Robustness using a Convolutional Neural Network

Troels H. P. Jensen, Helge T. Schmidt, Niels D. Bodin, Kamal Nasrollahi, Thomas B. Moeslund

2017

Abstract

With the number of privately owned cars increasing, the issue of locating an available parking space becomes apparant. This paper deals with the problem of verifying if a parking space is vacant, using a vision based system overlooking parking areas. In particular the paper proposes a binary classifier system, based on a Con- volutional Neural Network, that is capable of determining if a parking space is occupied or not. A benchmark database consisting of images captured from different parking areas, under different weather and illumina- tion conditions, has been used to train and test the system. The system shows promising performance on the database with an overall accuracy of 99.71 %

Download

Paper Citation

in Harvard Style

Jensen T., Schmidt H., Bodin N., Nasrollahi K. and Moeslund T. (2017). Parking Space Occupancy Verification - Improving Robustness using a Convolutional Neural Network . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-226-4, pages 311-318. DOI: 10.5220/0006135103110318

in Bibtex Style

@conference{visapp17,
author={Troels H. P. Jensen and Helge T. Schmidt and Niels D. Bodin and Kamal Nasrollahi and Thomas B. Moeslund},
title={Parking Space Occupancy Verification - Improving Robustness using a Convolutional Neural Network},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={311-318},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006135103110318},
isbn={978-989-758-226-4},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)
TI - Parking Space Occupancy Verification - Improving Robustness using a Convolutional Neural Network
SN - 978-989-758-226-4
AU - Jensen T.
AU - Schmidt H.
AU - Bodin N.
AU - Nasrollahi K.
AU - Moeslund T.
PY - 2017
SP - 311
EP - 318
DO - 10.5220/0006135103110318