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Dim Line Tracking Using Deep Learning for Autonomous Line Following Robot

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Artificial Intelligence Trends in Intelligent Systems (CSOC 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 573))

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Abstract

The proposed approach improves preprocessing of image data for the line following robot. The tracking algorithm uses Track–Before–Detect algorithm using Viterbi algorithm. Proposed technique uses deep learning for the estimation of the line and background area. The segmentation improves detection of weak line on the image disturbed by numerous additive patterns and Gaussian noise.

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Acknowledgments

This work is supported by the UE EFRR ZPORR project Z/2.32/I/1.3.1/267/05 “Szczecin University of Technology – Research and Education Center of Modern Multimedia Technologies” (Poland).

We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X GPU used for this research.

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Authors and Affiliations

  1. Department of Signal Processing and Multimedia Engineering, West–Pomeranian University of Technology, 26. Kwietnia 10 Street, 71126, Szczecin, Poland

    Grzegorz Matczak & Przemysław Mazurek

Authors
  1. Grzegorz Matczak
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  2. Przemysław Mazurek
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Corresponding author

Correspondence to Grzegorz Matczak .

Editor information

Editors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlin, Czech Republic

    Radek Silhavy

  2. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlin, Czech Republic

    Roman Senkerik

  3. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlin, Czech Republic

    Zuzana Kominkova Oplatkova

  4. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlin, Czech Republic

    Zdenka Prokopova

  5. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlin, Czech Republic

    Petr Silhavy

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Matczak, G., Mazurek, P. (2017). Dim Line Tracking Using Deep Learning for Autonomous Line Following Robot. In: Silhavy, R., Senkerik, R., Kominkova Oplatkova, Z., Prokopova, Z., Silhavy, P. (eds) Artificial Intelligence Trends in Intelligent Systems. CSOC 2017. Advances in Intelligent Systems and Computing, vol 573. Springer, Cham. https://doi.org/10.1007/978-3-319-57261-1_41

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  • DOI: https://doi.org/10.1007/978-3-319-57261-1_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57260-4

  • Online ISBN: 978-3-319-57261-1

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