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DriastSystem: A Computer Vision Based Device for Real Time Traffic Sign Detection and Recognition

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Artificial Intelligence and Soft Computing (ICAISC 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7267))

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Abstract

This paper presents the design and application of novel device for real time traffic sign detection and recognition on a hardware platform powered by Intel® AtomTM processor. Image frames from standard and relatively cheap web cameras are processed using OpenCV library [7][2]. An innovative method is proposed for traffic sign detection phase. Two color models are used for image segmentation and detection of traffic sign. Many well-known and described tactics have been tested and rated. Implemented in OpenCV Library functions for pattern recognition method are also used in main algorithm. Experimental results of traffic sign detection and recognition are described. The prototype was implemented as part of the Master Thesis at Cracow University of Technology [1].

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References

  1. Tekieli, M., Worek, K.: Design of Computer Vision Based Device for Real Time Traffic Sign Detection and Recognition. Master Thesis, Cracow University of Technology, Cracow (2011) (in Polish)

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  2. Bradski, G., Kaehler, A.: Learning OpenCV. O’Reilly (2008)

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  3. Siemens, VDO - Traffic Sign Recognition (2007), http://www.siemens.com/press/en/pp_cc/2007/02_feb/sosep200702_27_mt_special_mobility_1434304.htm

  4. Shneier, M.: Road Sign Detection and Recognition. In: ICCVPR 2005 (2005)

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  5. Yamada, K., Limpraptono, Y.: Intelligent Machine Vision System For Road Traffic Sign Recognition. In: Proc. of Seminar Nasional Teknoin (2008)

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  6. Nokia Corporation: Signals and Slots (2011), http://doc.qt.nokia.com/4.7/signalsandslots.html

  7. Willow Garage: OpenCV Wiki (2010), http://opencv.willowgarage.com/wiki/

  8. Cyganek, B.: Methods and Algorithms of Object Recognition in Digital Images. AGH University of Science and Technology Press (2009)

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  9. Brkic, K.: An overview of traffic sign detection methods (2010), http://www.fer.hr/_download/repository/BrkicQualifyingExam.pdf

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Author information

Authors and Affiliations

  1. Institute for Computational Civil Engineering, Faculty of Civil Engineering, Cracow University of Technology, Poland

    Marcin Tekieli & Marek Słoński

Authors
  1. Marcin Tekieli
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  2. Marek Słoński
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Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, Computer Science Division, University of California Berkeley, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Computational Intelligence Laboratory, Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

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© 2012 Springer-Verlag Berlin Heidelberg

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Tekieli, M., Słoński, M. (2012). DriastSystem: A Computer Vision Based Device for Real Time Traffic Sign Detection and Recognition. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7267. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29347-4_71

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  • DOI: https://doi.org/10.1007/978-3-642-29347-4_71

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29346-7

  • Online ISBN: 978-3-642-29347-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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