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    Tracking Multiple Objects with Kalman Filters, part II

    TitleTracking Multiple Objects with Kalman Filters, part II
    Publication TypeMaster Thesis
    Año de publicación2006
    Thesis Advisor(s)Marron, M
    AutoresLindeborg, M
    Idioma de publicaciónEnglish
    Institution

    University of Alcala

    School

    Escuela Politecnica Superior

    Degree

    Master in Electronics

    Academic DepartmentDepartment of Electronics
    Number of volumes1 vol.
    Páginas68
    Fecha de publicación02/2006
    Palabras claveErasmus, Kalman filters, Multi-Object Tracking, Real-time implementation, TFC
    Lugar de publicaciónAlcala de Henares (SPAIN)
    Resumen

    In this report the implementation of a multiple object tracking algorithm is described. The algorithm is part of the obstacle avoidance system in an autonomous robot. The measurement vector used to achieve the tracking task comes from a stereo-vision system that detects objects in the robot’s environment [1]. The algorithm uses the probabilistic Kalman filter (KF) to estimate the position and movement of different objects in the scene. One filter is used for each object to track. An algorithm for associating the data in the measurement vector to different objects is described. A validation process that the tracking algorithm uses to reduce the noise included in the measurement vector is also described.

    Abstract

    In this report the implementation of a multiple object tracking algorithm is described. The algorithm is part of the obstacle avoidance system in an autonomous robot. The measurement vector used to achieve the tracking task comes from a stereo-vision system that detects objects in the robot’s environment [1]. The algorithm uses the probabilistic Kalman filter (KF) to estimate the position and movement of different objects in the scene. One filter is used for each object to track. An algorithm for associating the data in the measurement vector to different objects is described. A validation process that the tracking algorithm uses to reduce the noise included in the measurement vector is also described.

    Tipo de trabajoMaster
    AttachmentSize
    Thesis_-_Micke.pdf562.29 KB