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Zebra Crossing Detection from Aerial Imagery Across Countries

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Computers Helping People with Special Needs (ICCHP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9759))

Abstract

We propose a data driven approach to detect zebra crossings in aerial imagery. The system automatically learns an appearance model from available geospatial data for an examined region. HOG as well as LBPH features, in combination with a SVM, yield state of the art detection results on different datasets. We also use this classifier across datasets obtained from different countries, to facilitate detections without requiring any additional geospatial data for that specific region. The approach is capable of searching for further, yet uncharted, zebra crossings in the data. Information gained from this work can be used to generate new zebra crossing databases or improve existing ones, which are especially useful in navigational assistance systems for visually impaired people. We show the usefulness of the proposed approach and plan to use this research as part of a larger guidance system.

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Notes

  1. 1.

    http://www.openstreetmap.org.

  2. 2.

    http://www.humanware.com.

  3. 3.

    http://www.aerowest.de.

  4. 4.

    http://maps.google.com.

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Authors and Affiliations

  1. Karlsruhe Institute of Technology, Karlsruhe, Germany

    Daniel Koester, Björn Lunt & Rainer Stiefelhagen

Authors
  1. Daniel Koester
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  2. Björn Lunt
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  3. Rainer Stiefelhagen
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Corresponding author

Correspondence to Daniel Koester .

Editor information

Editors and Affiliations

  1. Institute Integriert Studieren, Universität Linz, Linz, Austria

    Klaus Miesenberger

  2. Rehabilitationswissenschaften, Technische Universität Dortmund, Dortmund, Germany

    Christian Bühler

  3. Masaryk University , Brno, Czech Republic

    Petr Penaz

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Koester, D., Lunt, B., Stiefelhagen, R. (2016). Zebra Crossing Detection from Aerial Imagery Across Countries. In: Miesenberger, K., Bühler, C., Penaz, P. (eds) Computers Helping People with Special Needs. ICCHP 2016. Lecture Notes in Computer Science(), vol 9759. Springer, Cham. https://doi.org/10.1007/978-3-319-41267-2_5

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  • DOI: https://doi.org/10.1007/978-3-319-41267-2_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41266-5

  • Online ISBN: 978-3-319-41267-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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