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    A Bayesian Solution to Robustly Track Multiple Objects from Visual Data

    TitleA Bayesian Solution to Robustly Track Multiple Objects from Visual Data
    Publication TypeBook Chapter
    Año de publicación2008
    AutoresMarron, M, Garcia, JC, Sotelo, MA, Pizarro, D, Bravo, I, Martin, JL
    Book TitleINTELLIGENT TECHNIQUES AND TOOLS FOR NOVEL SYSTEM ARCHITECTURES
    Series TitleStudies in Computational Intelligence
    Volumen109
    Páginas531-547
    Fecha de publicación09/2008
    EditorialSpringer-Verlag.
    CityBerlin/Heidelberg (ALEMANIA)
    Idioma de publicaciónEnglish
    Numero ISBN978-3-540-77621-5
    Palabras claveartificial vision, bayesian estimation, Multi-Object Tracking
    Abstract

    Different solutions have been proposed for multiple objects tracking based on probabilistic algorithms. In this chapter, the authors propose the use of a single particle filter to track a variable number of objects in a complex environment.
    Estimator robustness and adaptability are both increased by the use of a clustering algorithm. Measurements used in the tracking process are extracted from a stereovision system, and thus, the 3D position of the tracked objects is obtained at each time step. As a proof of concept, real results are obtained in a long sequence with a mobile robot moving in a cluttered scene.

    Resumen

    Different solutions have been proposed for multiple objects tracking based on probabilistic algorithms. In this chapter, the authors propose the use of a single particle filter to track a variable number of objects in a complex environment.
    Estimator robustness and adaptability are both increased by the use of a clustering algorithm. Measurements used in the tracking process are extracted from a stereovision system, and thus, the 3D position of the tracked objects is obtained at each time step. As a proof of concept, real results are obtained in a long sequence with a mobile robot moving in a cluttered scene.

    URLhttp://www.springer.com/engineering/mathematical/book/978-3-540-77621-5
    DOI10.1007/978-3-540-77623-9_30
    AttachmentSize
    a_bayesian_solution-fulltext.pdf618.34 KB