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Towards Efficient Human Action Retrieval Based on Triplet-Loss Metric Learning

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Database and Expert Systems Applications (DEXA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13426))

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Abstract

Recent pose-estimation methods enable digitization of human motion by extracting 3D skeleton sequences from ordinary video recordings. Such spatio-temporal skeleton representation offers attractive possibilities for a wide range of applications but, at the same time, requires effective and efficient content-based access to make the extracted data reusable. In this paper, we focus on content-based retrieval of pre-segmented skeleton sequences of human actions to identify the most similar ones to a query action. We mainly deal with the extraction of content-preserving action features, which are learned using the triplet-loss approach in an unsupervised way. Such features are (1) effective as they achieve a similar retrieval quality as the features learned in a supervised way, and (2) of a fixed size which enables the application of indexing structures for efficient retrieval.

Supported by ERDF “CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence” (No. CZ.02.1.01/0.0/0.0/16_019/0000822).

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Authors and Affiliations

  1. Masaryk University, Botanicka 68a, 602 00, Brno, Czechia

    Iris Kico, Jan Sedmidubsky & Pavel Zezula

Authors
  1. Iris Kico
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  2. Jan Sedmidubsky
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  3. Pavel Zezula
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Corresponding author

Correspondence to Iris Kico .

Editor information

Editors and Affiliations

  1. University of Vienna, Vienna, Austria

    Christine Strauss

  2. University of Calabria, Rende, Italy

    Alfredo Cuzzocrea

  3. Johannes Kepler University of Linz, Linz, Austria

    Gabriele Kotsis

  4. Vienna University of Technology, Vienna, Austria

    A Min Tjoa

  5. Johannes Kepler University of Linz, Linz, Austria

    Ismail Khalil

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Kico, I., Sedmidubsky, J., Zezula, P. (2022). Towards Efficient Human Action Retrieval Based on Triplet-Loss Metric Learning. In: Strauss, C., Cuzzocrea, A., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2022. Lecture Notes in Computer Science, vol 13426. Springer, Cham. https://doi.org/10.1007/978-3-031-12423-5_18

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  • DOI: https://doi.org/10.1007/978-3-031-12423-5_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-12422-8

  • Online ISBN: 978-3-031-12423-5

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