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 proceedingArticle in proceedingResearchpeer-review

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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 languageEnglish
Title of host publicationProceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Volume5
PublisherSCITEPRESS Digital Library
Publication date2018
Pages311-318
ISBN (Print)978-989-758-226-4
DOIs
Publication statusPublished - 2018
EventInternational Conference on Computer Vision Theory and Applications - Porto, Portugal
Duration: 27 Feb 20171 Mar 2017
Conference number: 12
http://www.visapp.visigrapp.org

Conference

ConferenceInternational Conference on Computer Vision Theory and Applications
Number12
CountryPortugal
CityPorto
Period27/02/201701/03/2017
Internet address

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Parking
Neural networks
Classifiers
Railroad cars
Lighting

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

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 proceedingArticle in proceedingResearchpeer-review

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Jensen THP, Schmidt HT, Bodin ND, Nasrollahi K, Moeslund TB. 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. 2018. p. 311-318 https://doi.org/10.5220/0006135103110318