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IAPR Workshop on Artificial Neural Networks in Pattern Recognition

ANNPR 2012: Artificial Neural Networks in Pattern Recognition pp 193–200Cite as

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Traffic Sign Classifier Adaption by Semi-supervised Co-training

Traffic Sign Classifier Adaption by Semi-supervised Co-training

  • Matthias Hillebrand22,
  • Ulrich Kreßel22,
  • Christian Wöhler23 &
  • …
  • Franz Kummert24 
  • Conference paper
  • 1334 Accesses

  • 6 Citations

Part of the Lecture Notes in Computer Science book series (LNAI,volume 7477)

Abstract

The recognition of traffic signs in many state-of-the-art driver assistance systems is performed by statistical pattern classification methods. Traffic signs in European countries share many similarities but also vary in colour, size, and depicted symbols, making it hard to obtain one general classifier with good performance in all countries. Training separate classifiers for each country requires huge amounts of labelled training data. A well-trained classifier for one country can be adapted to other countries by semi-supervised learning methods to perform reasonably well with relatively low requirements regarding labelled training data. Self-training classifiers adapting themselves to unknown domains always risk that the adaption will become ineffective or even fail completely due to the occurrence of incorrectly labelled samples. To assure that self-training classifiers adapt themself correctly, advanced multi-classifier training methods like co-training are applied.

Keywords

  • self-training
  • semi-supervised
  • co-training

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Author information

Authors and Affiliations

  1. Group Research and Advanced Engineering, Daimler AG, 89081, Ulm, Germany

    Matthias Hillebrand & Ulrich Kreßel

  2. Image Analysis Group, TU Dortmund, 44221, Dortmund, Germany

    Christian Wöhler

  3. Applied Informatics Group, Bielefeld University, 33615, Bielefeld, Germany

    Franz Kummert

Authors
  1. Matthias Hillebrand
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  2. Ulrich Kreßel
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  3. Christian Wöhler
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  4. Franz Kummert
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Editor information

Editors and Affiliations

  1. Fondazione Bruno Kessler (FBK), 38123, Trento, Italy

    Nadia Mana

  2. Institute of Neural Information Processing, University of Ulm, 89069, Ulm, Germany

    Friedhelm Schwenker

  3. Dipartimento di Ingegneria dell’Informazione, Università di Siena, 53100, Siena, Italy

    Edmondo Trentin

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© 2012 Springer-Verlag Berlin Heidelberg

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Cite this paper

Hillebrand, M., Kreßel, U., Wöhler, C., Kummert, F. (2012). Traffic Sign Classifier Adaption by Semi-supervised Co-training. In: Mana, N., Schwenker, F., Trentin, E. (eds) Artificial Neural Networks in Pattern Recognition. ANNPR 2012. Lecture Notes in Computer Science(), vol 7477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33212-8_18

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  • DOI: https://doi.org/10.1007/978-3-642-33212-8_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33211-1

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