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Biternion Nets: Continuous Head Pose Regression from Discrete Training Labels

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Pattern Recognition (DAGM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9358))

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Abstract

While head pose estimation has been studied for some time, continuous head pose estimation is still an open problem. Most approaches either cannot deal with the periodicity of angular data or require very fine-grained regression labels. We introduce biternion nets, a CNN-based approach that can be trained on very coarse regression labels and still estimate fully continuous \({360}^{\circ }\) head poses. We show state-of-the-art results on several publicly available datasets. Finally, we demonstrate how easy it is to record and annotate a new dataset with coarse orientation labels in order to obtain continuous head pose estimates using our biternion nets.

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Notes

  1. 1.

    This becomes evident by computing the derivatives of the cost w.r.t. the parameters: the tilt and roll terms are absent from the derivative w.r.t. the pan and vice-versa.

  2. 2.

    Their setup is justified for their task, but makes a fair comparison impossible.

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Authors and Affiliations

  1. Visual Computing Institute, RWTH Aachen University, Aachen, Germany

    Lucas Beyer, Alexander Hermans & Bastian Leibe

Authors
  1. Lucas Beyer
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  2. Alexander Hermans
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  3. Bastian Leibe
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Corresponding author

Correspondence to Lucas Beyer .

Editor information

Editors and Affiliations

  1. Institute of Computer Science III, University of Bonn, Bonn, Germany

    Juergen Gall

  2. MPI for Intelligent Systems, University of Tübingen, Tübingen, Germany

    Peter Gehler

  3. Computer Vision Group, Visual Computing Institute, RWTH Aachen, Aachen, Germany

    Bastian Leibe

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Beyer, L., Hermans, A., Leibe, B. (2015). Biternion Nets: Continuous Head Pose Regression from Discrete Training Labels. In: Gall, J., Gehler, P., Leibe, B. (eds) Pattern Recognition. DAGM 2015. Lecture Notes in Computer Science(), vol 9358. Springer, Cham. https://doi.org/10.1007/978-3-319-24947-6_13

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  • DOI: https://doi.org/10.1007/978-3-319-24947-6_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24946-9

  • Online ISBN: 978-3-319-24947-6

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