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    Comparing Improved Versions of ‘K-Means’ and ‘Subtractive’ Clustering in a Tracking Application

    TitleComparing Improved Versions of ‘K-Means’ and ‘Subtractive’ Clustering in a Tracking Application
    Publication TypeJournal Article
    Año de publicación2007
    AutoresMarron, M, Sotelo, MA, Garcia, JC
    Idioma de publicaciónEnglish
    JournalLecture Notes in Computer Science
    Volumen4739
    Páginas717-724
    Fecha de publicación02/2007
    Lugar de publicaciónBerlin/Heideberg
    EditorialSpringer-Verlag
    Rank in category62/71
    JCR CategoryComputer Science, Theory & Methods
    Palabras claveclustering, Multi-Object Tracking, Particle Filters, Probabilistic Algorithms
    JCR Impact Factor0,402
    ISSN0302-9743
    URLhttp://www.springerlink.com/content/t372500g4j22/#section=379081&page=1&locus=0
    DOI10.1007/978-3-540-75867-9
    Abstract

    A partitional and a fuzzy clustering algorithm are compared in this
    paper in terms of accuracy, robustness and efficiency. 3D position data
    extracted from a stereo-vision system have to be clustered to use them in a
    tracking application in which a particle filter is the kernel of the estimation task.
    ‘K-Means’ and ‘Subtractive’ algorithms have been modified and enriched with
    a validation process in order improve its functionality in the tracking system.
    Comparisons and conclusions of the clustering results both in a stand-alone
    process and in the proposed tracking task are shown in the paper.

    Resumen

    A partitional and a fuzzy clustering algorithm are compared in this
    paper in terms of accuracy, robustness and efficiency. 3D position data
    extracted from a stereo-vision system have to be clustered to use them in a
    tracking application in which a particle filter is the kernel of the estimation task.
    ‘K-Means’ and ‘Subtractive’ algorithms have been modified and enriched with
    a validation process in order improve its functionality in the tracking system.
    Comparisons and conclusions of the clustering results both in a stand-alone
    process and in the proposed tracking task are shown in the paper.

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