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A Two-Stage Approach for Commonality-Based Temporal Localization of Periodic Motions

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Computer Vision Systems (ICVS 2019)

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

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Abstract

We present an unsupervised method for the detection of all temporal segments of videos or motion capture data, that correspond to periodic motions. The proposed method is based on the detection of similar segments (commonalities) in different parts of the input sequence and employs a two-stage approach that operates on the matrix of pairwise distances of all input frames. The quantitative evaluation of the proposed method on three standard ground-truth-annotated datasets (two video datasets, one 3D human motion capture dataset) demonstrate its improved performance in comparison to existing approaches.

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Acknowledgments

This work was partially supported by the EU project Co4Robots (H2020-731869).

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Authors and Affiliations

  1. Institute of Computer Science, FORTH, Heraklion, Greece

    Costas Panagiotakis & Antonis Argyros

  2. Department of Management Science and Technology, Hellenic Mediterranean University, Heraklion, Greece

    Costas Panagiotakis

  3. Computer Science Department, University of Crete, Heraklion, Greece

    Antonis Argyros

Authors
  1. Costas Panagiotakis
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  2. Antonis Argyros
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Corresponding author

Correspondence to Costas Panagiotakis .

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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Panagiotakis, C., Argyros, A. (2019). A Two-Stage Approach for Commonality-Based Temporal Localization of Periodic Motions. 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_33

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  • DOI: https://doi.org/10.1007/978-3-030-34995-0_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34994-3

  • Online ISBN: 978-3-030-34995-0

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