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Pedestrian Orientation Estimation

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Pattern Recognition (GCPR 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8753))

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Abstract

This paper addresses the task of estimating the orientation of pedestrians from monocular images provided by an automotive camera. From an initial detection of a pedestrian, we analyze the area within their bounding box and give an estimation of the orientation. Using ground truth mocap data, we define the orientations as a direction and a rough human pose. A random forest classifier trained on this data using HOG features assigns each detected pedestrian to their orientation cluster. Evaluation of the method is performed on a new dataset and on a publicly available dataset showing improved results.

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Notes

  1. 1.

    http://www.nhtsa.gov/Pedestrians

  2. 2.

    http://ec.europa.eu/transport/road_safety/index_en.htm

  3. 3.

    www.ar-tracking.com

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Author information

Authors and Affiliations

  1. BMW Group, Munich, Germany

    Joe Lallemand, Alexandra Ronge & Magdalena Szczot

  2. Computer Aided Medical Procedures, Technische Universität München, Munich, Germany

    Joe Lallemand & Slobodan Ilic

  3. Siemens AG, Munich, Germany

    Slobodan Ilic

Authors
  1. Joe Lallemand
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  2. Alexandra Ronge
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  3. Magdalena Szczot
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  4. Slobodan Ilic
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Corresponding author

Correspondence to Joe Lallemand .

Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Münster, Münster, Germany

    Xiaoyi Jiang

  2. Computer Science Department 5, University of Erlangen-Nürnberg, Erlangen, Germany

    Joachim Hornegger

  3. Department of Computer Science, University of Kiel, Kiel, Germany

    Reinhard Koch

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Lallemand, J., Ronge, A., Szczot, M., Ilic, S. (2014). Pedestrian Orientation Estimation. In: Jiang, X., Hornegger, J., Koch, R. (eds) Pattern Recognition. GCPR 2014. Lecture Notes in Computer Science(), vol 8753. Springer, Cham. https://doi.org/10.1007/978-3-319-11752-2_39

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  • DOI: https://doi.org/10.1007/978-3-319-11752-2_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11751-5

  • Online ISBN: 978-3-319-11752-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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