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Walker-Independent Features for Gait Recognition from Motion Capture Data

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F16%3A00090768" target="_blank" >RIV/00216224:14330/16:00090768 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1007/978-3-319-49055-7_28" target="_blank" >https://doi.org/10.1007/978-3-319-49055-7_28</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-319-49055-7_28" target="_blank" >10.1007/978-3-319-49055-7_28</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Walker-Independent Features for Gait Recognition from Motion Capture Data

  • Original language description

    MoCap-based human identification, as a pattern recognition discipline, can be optimized using a machine learning approach. Yet in some applications such as video surveillance new identities can appear on the fly and labeled data for all encountered people may not always be available. This work introduces the concept of learning walker-independent gait features directly from raw joint coordinates by a modification of the Fisher’s Linear Discriminant Analysis with Maximum Margin Criterion. Our new approach shows not only that these features can discriminate different people than who they are learned on, but also that the number of learning identities can be much smaller than the number of walkers encountered in the real operation.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    IN - Informatics

  • OECD FORD branch

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2016

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Proceedings of the joint IAPR International Workshops on Structural and Syntactic Pattern Recognition (SSPR 2016) and Statistical Techniques in Pattern Recognition (SPR 2016)

  • ISBN

    9783319490540

  • ISSN

    0302-9743

  • e-ISSN

  • Number of pages

    12

  • Pages from-to

    310-321

  • Publisher name

    Springer International Publishing AG

  • Place of publication

    Switzerland

  • Event location

    Mérida, Mexico

  • Event date

    Jan 1, 2016

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article