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Real-Time Modelling and Processing for Communication Systems pp 257–282Cite as

Real-Time Implementation of Light-Independent Traffic Sign Recognition Approach

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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 29))

Abstract

In order to guarantee the safety of road users (both pedestrians and drivers), one key element is Traffic Signs Recognition. Computer vision for driving assistance offered significant progress in road sign detection, but still needs great improvements because of difficulties associated with extreme variations in lighting conditions. When poor lighting conditions are met, the driver must be alerted when a road sign is encountered. This is feasible through an automatic system equipped with a camera on the dashboard of the vehicle, capable of detecting the road sign and alarming the driver. This chapter’s main objective is the development of an adequate and robust Traffic Signs Recognition system functioning independently of lighting situations. A three task approach is proposed, it is mainly composed of: object detection, shape classification and content classification. The detection phase is based on the RGB-color space segmentation with an empirically determined threshold. It extracts the relevant red and blue regions in the image with limit values of Bounding Boxes (BB). After object extraction, the sign’s shape is classified by an artificial neural network (ANN). Road signs are classified according to their shape characteristics, as triangular, squared and circular shapes. The classified circular and triangular shapes are passed on to the second ANN in the third phase. These identify the pictogram of the road sign. The output of the second ANN allows the full recognition of the traffic sign. The algorithm proposed and its performances are tested and discussed in a dataset of real driving scenarios which captured in various weather conditions.

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Author information

Authors and Affiliations

  1. Microelectronics & Instrumentation Laboratory, Faculty of Sciences of Monastir, National Engineering School of Sousse, University of Sousse, Sousse, Tunisia

    S. Hamdi & W. Farhat

  2. Microelectronics & Instrumentation Laboratory, Faculty of Sciences of Monastir, Higher Institute of Applied Sciences and Technology of Sousse, University of Sousse, Sousse, Tunisia

    H. Faeidh & C. Souani

Authors
  1. S. Hamdi
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  2. H. Faeidh
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  3. W. Farhat
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  4. C. Souani
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Corresponding author

Correspondence to S. Hamdi .

Editor information

Editors and Affiliations

  1. School of Cyberspace, Hangzhou Dianzi University, Hangzhou, Zhejiang, China

    Muhammad Alam

  2. Department of Electronics, Institut Supérieur des Sciences Appliquées et de Technologie de Sousse, Université de Sousse, Sousse , Tunisia

    Wael Dghais

  3. School of Cyberspace, Hangzhou Dianzi University, Hangzhou, Zhejiang, China

    Yuanfang Chen

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Hamdi, S., Faeidh, H., Farhat, W., Souani, C. (2018). Real-Time Implementation of Light-Independent Traffic Sign Recognition Approach. In: Alam, M., Dghais, W., Chen, Y. (eds) Real-Time Modelling and Processing for Communication Systems. Lecture Notes in Networks and Systems, vol 29. Springer, Cham. https://doi.org/10.1007/978-3-319-72215-3_10

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  • DOI: https://doi.org/10.1007/978-3-319-72215-3_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72214-6

  • Online ISBN: 978-3-319-72215-3

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