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International Conference on E-Business and Telecommunications

ICETE 2015: E-Business and Telecommunications pp 454–467Cite as

Traffic Signs Detection Using Tracking with Prediction

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 585))

Abstract

This paper proposes an efficient algorithm for real-time traffic sign detection. The article considers the practicability of using HSV color space to extract the red color. An algorithm to remove noise to improve the accuracy and speed of detection was developed. A modified Generalized Hough transform is then used to detect traffic signs. The current velocity of a vehicle is used to predict the sign’s location in the adjacent frames in a video sequence. Finally, the detected objects are being classified. The detection and classification of road signs algorithms are implemented using CUDA and operate in real time on an Android device. The developed algorithms have been tested using real scene images and the German Traffic Sign Detection Benchmark (GTSDB) dataset and showed efficient results.

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Acknowledgements

This work was supported by Project #RFMEFI57514X0083 by the Ministry of Education and Science of the Russian Federation.

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Authors and Affiliations

  1. Samara State Aerospace University, 34 Moskovskoye Shosse, Samara, Russia

    Pavel Yakimov

  2. Image Processing Systems Institute of Russian Academy of Sciences, 151 Molodogvardeyskaya Street, Samara, Russia

    Pavel Yakimov

Authors
  1. Pavel Yakimov
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Corresponding author

Correspondence to Pavel Yakimov .

Editor information

Editors and Affiliations

  1. Dept. of Computer & Info Science, Fordham University, Bronx, New York, USA

    Mohammad S. Obaidat

  2. IUT, University of Haute Alsace, Colmar, France

    Pascal Lorenz

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© 2016 Springer International Publishing Switzerland

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Yakimov, P. (2016). Traffic Signs Detection Using Tracking with Prediction. In: Obaidat, M., Lorenz, P. (eds) E-Business and Telecommunications. ICETE 2015. Communications in Computer and Information Science, vol 585. Springer, Cham. https://doi.org/10.1007/978-3-319-30222-5_21

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  • DOI: https://doi.org/10.1007/978-3-319-30222-5_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30221-8

  • Online ISBN: 978-3-319-30222-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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