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An Evaluation Framework and Database for MoCap-Based Gait Recognition Methods

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F17%3A00095907" target="_blank" >RIV/00216224:14330/17:00095907 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1007/978-3-319-56414-2_3" target="_blank" >https://doi.org/10.1007/978-3-319-56414-2_3</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-319-56414-2_3" target="_blank" >10.1007/978-3-319-56414-2_3</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    An Evaluation Framework and Database for MoCap-Based Gait Recognition Methods

  • Original language description

    As a contribution to reproducible research, this paper presents a framework and a database to improve the development, evaluation and comparison of methods for gait recognition from Motion Capture (MoCap) data. The evaluation framework provides implementation details and source codes of state-of-the-art human-interpretable geometric features as well as our own approaches where gait features are learned by a modification of Fisher's Linear Discriminant Analysis with the Maximum Margin Criterion, and by a combination of Principal Component Analysis and Linear Discriminant Analysis. It includes a description and source codes of a mechanism for evaluating four class separability coefficients of feature space and four rank-based classifier performance metrics. This framework also contains a tool for learning a custom classifier and for classifying a custom query on a custom gallery. We provide an experimental database along with source codes for its extraction from the general CMU MoCap database.

  • 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

  • Continuities

    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

    Proceedings of the 1st IAPR Workshop on Reproducible Research in Pattern Recognition (RRPR 2016)

  • ISBN

    9783319564135

  • ISSN

    0302-9743

  • e-ISSN

  • Number of pages

    15

  • Pages from-to

    33-47

  • Publisher name

    Springer International Publishing AG

  • Place of publication

    Switzerland

  • Event location

    Cancun, Mexico

  • Event date

    Jan 1, 2016

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000426089600003