Parking Space Verification: Improving Robustness Using A Convolutional Neural Network

Troels Høg Peter Jensen; Helge Thomsen Schmidt; Niels Dyremose Bodin; Kamal Nasrollahi; Thomas B. Moeslund

doi:10.5220/0006135103110318

Parking Space Verification: Improving Robustness Using A Convolutional Neural Network

Troels Høg Peter Jensen, Helge Thomsen Schmidt, Niels Dyremose Bodin, Kamal Nasrollahi, Thomas B. Moeslund

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

7 Citations (Scopus)

1011 Downloads (Pure)

Abstract

With the number of privately owned cars increasing, the issue of locating an available parking space becomes apparant. This paper deals with the verification of vacant parking spaces, by using a vision based system looking over parking areas. In particular the paper proposes a binary classifier system, based on a Convolutional 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 illumination conditions, has been used to train and test the system. The system shows promising performance on the database with an accuracy of 99.71% overall and is robust to the variations in parking areas and weather conditions.

Original language	English
Title of host publication	Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Volume	5
Publisher	SCITEPRESS Digital Library
Publication date	2018
Pages	311-318
ISBN (Print)	978-989-758-226-4
DOIs	https://doi.org/10.5220/0006135103110318
Publication status	Published - 2018
Event	International Conference on Computer Vision Theory and Applications - Porto, Portugal Duration: 27 Feb 2017 → 1 Mar 2017 Conference number: 12 http://www.visapp.visigrapp.org

Conference

Conference	International Conference on Computer Vision Theory and Applications
Number	12
Country/Territory	Portugal
City	Porto
Period	27/02/2017 → 01/03/2017
Internet address	http://www.visapp.visigrapp.org

Access to Document

10.5220/0006135103110318

ParkingAccepted author manuscript, 1.07 MB

AUB Link

Search for the material in Aalborg University Library's search engine

Cite this

Jensen, T. H. P., Schmidt, H. T., Bodin, N. D., Nasrollahi, K., & Moeslund, T. B. (2018). Parking Space 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 (Vol. 5, pp. 311-318). SCITEPRESS Digital Library. https://doi.org/10.5220/0006135103110318

@inproceedings{5c28bcf2a1af4f06aea219b75a96216d,

title = "Parking Space Verification: Improving Robustness Using A Convolutional Neural Network",

abstract = "With the number of privately owned cars increasing, the issue of locating an available parking space becomes apparant. This paper deals with the verification of vacant parking spaces, by using a vision based system looking over parking areas. In particular the paper proposes a binary classifier system, based on a Convolutional 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 illumination conditions, has been used to train and test the system. The system shows promising performance on the database with an accuracy of 99.71% overall and is robust to the variations in parking areas and weather conditions.",

author = "Jensen, {Troels H{\o}g Peter} and Schmidt, {Helge Thomsen} and Bodin, {Niels Dyremose} and Kamal Nasrollahi and Moeslund, {Thomas B.}",

year = "2018",

doi = "10.5220/0006135103110318",

language = "English",

isbn = "978-989-758-226-4",

volume = "5",

pages = "311--318",

booktitle = "Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications",

publisher = "SCITEPRESS Digital Library",

note = "International Conference on Computer Vision Theory and Applications, VISAPP ; Conference date: 27-02-2017 Through 01-03-2017",

url = "http://www.visapp.visigrapp.org",

}

Jensen, THP, Schmidt, HT, Bodin, ND, Nasrollahi, K & Moeslund, TB 2018, Parking Space 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. vol. 5, SCITEPRESS Digital Library, pp. 311-318, International Conference on Computer Vision Theory and Applications, Porto, Portugal, 27/02/2017. https://doi.org/10.5220/0006135103110318

Parking Space Verification: Improving Robustness Using A Convolutional Neural Network. / Jensen, Troels Høg Peter; Schmidt, Helge Thomsen; Bodin, Niels Dyremose et al.
Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. Vol. 5 SCITEPRESS Digital Library, 2018. p. 311-318.

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - Parking Space Verification

T2 - International Conference on Computer Vision Theory and Applications

AU - Jensen, Troels Høg Peter

AU - Schmidt, Helge Thomsen

AU - Bodin, Niels Dyremose

AU - Nasrollahi, Kamal

AU - Moeslund, Thomas B.

N1 - Conference code: 12

PY - 2018

Y1 - 2018

N2 - With the number of privately owned cars increasing, the issue of locating an available parking space becomes apparant. This paper deals with the verification of vacant parking spaces, by using a vision based system looking over parking areas. In particular the paper proposes a binary classifier system, based on a Convolutional 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 illumination conditions, has been used to train and test the system. The system shows promising performance on the database with an accuracy of 99.71% overall and is robust to the variations in parking areas and weather conditions.

AB - With the number of privately owned cars increasing, the issue of locating an available parking space becomes apparant. This paper deals with the verification of vacant parking spaces, by using a vision based system looking over parking areas. In particular the paper proposes a binary classifier system, based on a Convolutional 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 illumination conditions, has been used to train and test the system. The system shows promising performance on the database with an accuracy of 99.71% overall and is robust to the variations in parking areas and weather conditions.

U2 - 10.5220/0006135103110318

DO - 10.5220/0006135103110318

M3 - Article in proceeding

SN - 978-989-758-226-4

VL - 5

SP - 311

EP - 318

BT - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

PB - SCITEPRESS Digital Library

Y2 - 27 February 2017 through 1 March 2017

ER -

Parking Space Verification: Improving Robustness Using A Convolutional Neural Network

Abstract

Conference

Access to Document

AUB Link

Fingerprint

Cite this