Geintra

Departamento de electronica Universidad de Alcala

Research lines

Access information on the Geintra research activity structure. More information


Work with us

Access to our current offer of grants and contracts. More information


Contact

You can contact us using different means. More information

    Tracking Multiple Objects with Kalman Filters, part I

    TitleTracking Multiple Objects with Kalman Filters, part I
    Publication TypeMaster Thesis
    Año de publicación2006
    Thesis Advisor(s)Marron, M
    AutoresBroddfelt, J
    Idioma de publicaciónEnglish
    InstitutionUniversity of Alcala
    SchoolEscuela Politecnica Superior
    DegreeMaster in Electronics
    Academic DepartmentDepartment of Electronics
    Number of volumes1 vol.
    Páginas85
    Fecha de publicación02/2006
    Palabras claveErasmus, Kalman filters, Multi-Object Tracking, TFC
    Lugar de publicaciónAlcala de Henares (SPAIN)
    Resumen

    There are many different solutions for tracking multiple objects and many of these solutions involve probabilistic algorithms, which have been fully tested as the best solution in tracking tasks. In this thesis a multiple tracking algorithm based on the Kalman Filter and the Probabilistic Data Association Filter is developed. The algorithm is part of an obstacle avoidance system in an autonomous robot. The measurements used as input to the tracking algorithm come from a stereo-vision system that detects objects in the robot’s environment. The robustness and adaptability of the tracking algorithm is increased by the use of a validation/removal algorithm. The algorithm is capable of initiating tracks, accounting for false reports, and removing tracks, accounting for missing reports.

    Abstract

     

     

     

    There are many different solutions for tracking multiple objects and many of these solutions involve probabilistic algorithms, which have been fully tested as the best solution in tracking tasks. In this thesis a multiple tracking algorithm based on the Kalman Filter and the Probabilistic Data Association Filter is developed. The algorithm is part of an obstacle avoidance system in an autonomous robot. The measurements used as input to the tracking algorithm come from a stereo-vision system that detects objects in the robot’s environment. The robustness and adaptability of the tracking algorithm is increased by the use of a validation/removal algorithm. The algorithm is capable of initiating tracks, accounting for false reports, and removing tracks, accounting for missing reports.

    Tipo de trabajoMaster
    AttachmentSize
    Thesis_-_Johanna.pdf1.22 MB

    Geintra © 2008-2015

    Diseño web por Hazhistoria