Assistance System for Traffic Signs Inventory

Karel Zídek, Tomáš Koubek, David Procházka and Marcel Vytečka
Additional contact information
Karel Zídek: Department of Informatics, Faculty of Business and Economics, Mendel University in Brno, Zemědělská 1, 613 00 Brno, Czech Republic
Tomáš Koubek: Department of Informatics, Faculty of Business and Economics, Mendel University in Brno, Zemědělská 1, 613 00 Brno, Czech Republic
Marcel Vytečka: Department of Informatics, Faculty of Business and Economics, Mendel University in Brno, Zemědělská 1, 613 00 Brno, Czech Republic

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2015, vol. 63, issue 6, 2197-2204

Abstract: We can see arising trend in the automotive industry in last years - autonomous cars that are driven just by on-board computers. The traffic signs tracking system must deal with real conditions with data that are frequently obtained in poor light conditions, fog, heavy rain or are otherwise disturbed. Completely same problem is solved by mapping companies that are producing geospatial data for different information systems, navigations, etc. They are frequently using cars equipped with a wide range of measuring instruments including panoramic cameras. These measurements are frequently done during early morning hours when the traffic conditions are acceptable. However, in this time, the sun position is usually not optimal for the photography. Most of the traffic signs and other street objects are heavily underexposed. Hence, it is difficult to find an automatic approach that can identify them reliably. In this article, we focus on methods designed to deal with the described conditions. An overview of the state-of-the-art methods is outlined. Further, where it is possible, we outline an implementation of the described methods using well-known Open Computer Vision library. Finally, emphasis is placed on the methods that can deal with low light conditions, fog or other situations that complicate the detection process.

Keywords: OpenCV; traffic signs; image processing; object recognition; road inventory; machine learning; Viola-Jones detector; support vector machines (search for similar items in EconPapers)
Date: 2015
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://acta.mendelu.cz/doi/10.11118/actaun201563062197.html (text/html)
http://acta.mendelu.cz/doi/10.11118/actaun201563062197.pdf (application/pdf)
free of charge

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:mup:actaun:actaun_2015063062197

DOI: 10.11118/actaun201563062197

Access Statistics for this article

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis is currently edited by Markéta Havlásková

More articles in Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis from Mendel University Press
Bibliographic data for series maintained by Ivo Andrle ().