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GPU implementation of a road sign detector based on particle swarm optimization

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Abstract

Road Sign Detection is a major goal of the Advanced Driving Assistance Systems. Most published work on this problem share the same approach by which signs are first detected and then classified in video sequences, even if different techniques are used. While detection is usually performed using classical computer vision techniques based on color and/or shape matching, most often classification is performed by neural networks. In this work we present a novel modular and scalable approach to road sign detection based on Particle Swarm Optimization, which takes into account both shape and color to detect signs. In our approach, in particular, the optimization of a single fitness function allows both to detect a sign belonging to a certain category and, at the same time, to estimate its position with respect to the camera reference frame. To speed up processing, the algorithm implementation exploits the parallel computing capabilities offered by modern graphics cards and, in particular, by the Compute Unified Device Architecture by nVIDIA. The effectiveness of the approach has been assessed on both synthetic and real video sequences, which have been successfully processed at, or close to, full frame rate.

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Notes

  1. http://www.povray.org.

  2. http://lib.povray.org/collection/roadsigns/chrisb2.0/roadsigns.html.

  3. http://f-lohmueller.de/pov_tut/objects/obj_500i.htm.

  4. http://www.vislab.it.

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Acknowledgments

We would like to express our thanks and appreciation to Gabriele Novelli, Denis Simonazzi and Marco Tovagliari for their help in tuning, testing and assessing the performances of our system.

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Authors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione, University of Parma, Viale G. Usberti 181a, 43124, Parma, Italy

    Luca Mussi, Stefano Cagnoni, Elena Cardarelli, Paolo Medici & Pier Paolo Porta

  2. Information Systems Institute (ISI) - HEC, University of Lausanne, Internef 135, CH-1015, Lausanne, Switzerland

    Fabio Daolio

Authors
  1. Luca Mussi
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  2. Stefano Cagnoni
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  3. Elena Cardarelli
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  4. Fabio Daolio
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  5. Paolo Medici
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  6. Pier Paolo Porta
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Correspondence to Stefano Cagnoni.

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Mussi, L., Cagnoni, S., Cardarelli, E. et al. GPU implementation of a road sign detector based on particle swarm optimization. Evol. Intel. 3, 155–169 (2010). https://doi.org/10.1007/s12065-010-0043-y

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  • DOI: https://doi.org/10.1007/s12065-010-0043-y

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