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A Distributed Wireless Camera System for the Management of Parking Spaces

The importance of detection of parking space availability is still growing, particularly in major cities. This paper deals with the design of a distributed wireless camera system for the management of parking spaces, which can determine occupancy of the parking space based on the information from mu...

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Detalles Bibliográficos
Autores principales: Vítek, Stanislav, Melničuk, Petr
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5795336/
https://www.ncbi.nlm.nih.gov/pubmed/29283371
http://dx.doi.org/10.3390/s18010069
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author Vítek, Stanislav
Melničuk, Petr
author_facet Vítek, Stanislav
Melničuk, Petr
author_sort Vítek, Stanislav
collection PubMed
description The importance of detection of parking space availability is still growing, particularly in major cities. This paper deals with the design of a distributed wireless camera system for the management of parking spaces, which can determine occupancy of the parking space based on the information from multiple cameras. The proposed system uses small camera modules based on Raspberry Pi Zero and computationally efficient algorithm for the occupancy detection based on the histogram of oriented gradients (HOG) feature descriptor and support vector machine (SVM) classifier. We have included information about the orientation of the vehicle as a supporting feature, which has enabled us to achieve better accuracy. The described solution can deliver occupancy information at the rate of 10 parking spaces per second with more than 90% accuracy in a wide range of conditions. Reliability of the implemented algorithm is evaluated with three different test sets which altogether contain over 700,000 samples of parking spaces.
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spelling pubmed-57953362018-02-13 A Distributed Wireless Camera System for the Management of Parking Spaces Vítek, Stanislav Melničuk, Petr Sensors (Basel) Article The importance of detection of parking space availability is still growing, particularly in major cities. This paper deals with the design of a distributed wireless camera system for the management of parking spaces, which can determine occupancy of the parking space based on the information from multiple cameras. The proposed system uses small camera modules based on Raspberry Pi Zero and computationally efficient algorithm for the occupancy detection based on the histogram of oriented gradients (HOG) feature descriptor and support vector machine (SVM) classifier. We have included information about the orientation of the vehicle as a supporting feature, which has enabled us to achieve better accuracy. The described solution can deliver occupancy information at the rate of 10 parking spaces per second with more than 90% accuracy in a wide range of conditions. Reliability of the implemented algorithm is evaluated with three different test sets which altogether contain over 700,000 samples of parking spaces. MDPI 2017-12-28 /pmc/articles/PMC5795336/ /pubmed/29283371 http://dx.doi.org/10.3390/s18010069 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Vítek, Stanislav
Melničuk, Petr
A Distributed Wireless Camera System for the Management of Parking Spaces
title A Distributed Wireless Camera System for the Management of Parking Spaces
title_full A Distributed Wireless Camera System for the Management of Parking Spaces
title_fullStr A Distributed Wireless Camera System for the Management of Parking Spaces
title_full_unstemmed A Distributed Wireless Camera System for the Management of Parking Spaces
title_short A Distributed Wireless Camera System for the Management of Parking Spaces
title_sort distributed wireless camera system for the management of parking spaces
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5795336/
https://www.ncbi.nlm.nih.gov/pubmed/29283371
http://dx.doi.org/10.3390/s18010069
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