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Effective and Efficient Similarity Searching 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%2F18%3A00100703" target="_blank" >RIV/00216224:14330/18:00100703 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1007/s11042-017-4859-7" target="_blank" >http://dx.doi.org/10.1007/s11042-017-4859-7</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/s11042-017-4859-7" target="_blank" >10.1007/s11042-017-4859-7</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Effective and Efficient Similarity Searching in Motion Capture Data

  • Original language description

    Motion capture data describe human movements in the form of spatio-temporal trajectories of skeleton joints. Intelligent management of such complex data is a challenging task for computers which requires an effective concept of motion similarity. However, evaluating the pair-wise similarity is a difficult problem as a single action can be performed by various actors in different ways, speeds or starting positions. Recent methods usually model the motion similarity by comparing customized features using distance-based functions or specialized machine-learning classifiers. By combining both these approaches, we transform the problem of comparing motions of variable sizes into the problem of comparing fixed-size vectors. Specifically, each rather-short motion is encoded into a compact visual representation from which a highly descriptive 4,096-dimensional feature vector is extracted using a fine-tuned deep convolutional neural network. The advantage is that the fixed-size features are compared by the Euclidean distance which enables efficient motion indexing by any metric-based index structure. Another advantage of the proposed approach is its tolerance towards an imprecise action segmentation, the variance in movement speed, and a lower data quality. All these properties together bring new possibilities for effective and efficient large-scale retrieval.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • 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)

Others

  • Publication year

    2018

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    Multimedia Tools and Applications

  • ISSN

    1380-7501

  • e-ISSN

    1573-7721

  • Volume of the periodical

    77

  • Issue of the periodical within the volume

    10

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    22

  • Pages from-to

    12073-12094

  • UT code for WoS article

    000433202100021

  • EID of the result in the Scopus database

    2-s2.0-85019711344