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Enhancing Effectiveness of Descriptors for Searching and Recognition in 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%2F17%3A00094977" target="_blank" >RIV/00216224:14330/17:00094977 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1109/ISM.2017.39" target="_blank" >http://dx.doi.org/10.1109/ISM.2017.39</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/ISM.2017.39" target="_blank" >10.1109/ISM.2017.39</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Enhancing Effectiveness of Descriptors for Searching and Recognition in Motion Capture Data

  • Original language description

    Computer-aided analyses of motion capture data require an effective and efficient concept of motion similarity. Traditional methods generally compare motion sequences by applying time-warping techniques to high-dimensional trajectories of joints. An increasing effectiveness of machine-learning techniques, such as deep convolutional neural networks, brings new possibilities for similarity comparison. Inspired by recent advances in neural networks and image processing, we propose new variants of transformation of motion sequences into 2D images. The generated images are used to fine-tune a neural network from which 4,096D features are extracted and compared by a modified Euclidean distance. The proposed concept is not only efficient but also very effective and outperforms existing methods on a challenging dataset with 130 categories.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    <a href="/en/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Center for Large Scale Multi-modal Data Interpretation</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2017

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    19th IEEE International Symposium on Multimedia

  • ISBN

    9781538629376

  • ISSN

  • e-ISSN

  • Number of pages

    4

  • Pages from-to

    240-243

  • Publisher name

    IEEE Computer Society

  • Place of publication

    Neuveden

  • Event location

    Taichung, Taiwan

  • Event date

    Jan 1, 2017

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article