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Deep Residual Temporal Convolutional Networks for Skeleton-Based Human Action Recognition

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Part of the book series:Lecture Notes in Computer Science ((LNTCS,volume 11754))

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Abstract

Deep residual networks for action recognition based on skeleton data can avoid the degradation problem, and a 56-layer Res-Net has recently achieved good results. Since a much “shallower” 11-layer model (Res-TCN) with a temporal convolution network and a simplified residual unit achieved almost competitive performance, we investigate deep variants of Res-TCN and compare them to Res-Net architectures. Our results outperform the other approaches in this class of residual networks. Our investigation suggests that the resistance of deep residual networks to degradation is not only determined by the architecture but also by data and task properties.

This work has been supported by the German Aerospace Center (DLR) with financial means of the German Federal Ministry for Economic Affairs and Energy (BMWi), project “OPA3L” (grant No. 50 NA 1909) and by the German Research Foundation DFG, as part of CRC (Sonderforschungsbereich) 1320 “EASE - Everyday Activity Science and Engineering”, University of Bremen (http://www.ease-crc.org/).

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Authors and Affiliations

  1. Cognitive Neuroinformatics, University of Bremen, Bremen, Germany

    R. Khamsehashari, K. Gadzicki & C. Zetzsche

Authors
  1. R. Khamsehashari

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  2. K. Gadzicki

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  3. C. Zetzsche

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Corresponding author

Correspondence toR. Khamsehashari.

Editor information

Editors and Affiliations

  1. Centre for Research and Technology Hellas (CERTH-ITI), Thessaloniki, Greece

    Dimitrios Tzovaras

  2. Centre for Research and Technology Hellas (CERTH-ITI), Thessaloniki, Greece

    Dimitrios Giakoumis

  3. Vienna University of Technology, Vienna, Austria

    Markus Vincze

  4. Foundation for Research and Technology Hellas (FORTH), Heraklion, Greece

    Antonis Argyros

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Khamsehashari, R., Gadzicki, K., Zetzsche, C. (2019). Deep Residual Temporal Convolutional Networks for Skeleton-Based Human Action Recognition. In: Tzovaras, D., Giakoumis, D., Vincze, M., Argyros, A. (eds) Computer Vision Systems. ICVS 2019. Lecture Notes in Computer Science(), vol 11754. Springer, Cham. https://doi.org/10.1007/978-3-030-34995-0_34

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eBook
JPY 5719
Price includes VAT (Japan)
  • Available as EPUB and PDF
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Softcover Book
JPY 7149
Price includes VAT (Japan)
  • Compact, lightweight edition
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