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    Robust People Detection and Tracking from an overhead Time-of-Flight Camera

    TítuloRobust People Detection and Tracking from an overhead Time-of-Flight Camera
    Tipo de publicaciónConference Paper
    Año de publicación2017
    AutoresFernandez-Rincon, A, Fuentes-Jimenez, D, Losada-Gutierrez, C, Marron-Romera, M, Luna, CA, Macias-Guarasa, J, Mazo, M
    Idioma de publicaciónEnglish
    Conference Name12th International Conference on Computer Vision Theory and Applications.
    Volumen4
    Páginas556-564
    Conference LocationPorto, Portugal
    Fecha de publicación03/2017
    Numero ISBN978-989-758-225-7
    DOI10.5220/0006169905560564
    Resumen

    In this paper we describe a system for robust detection of people in a scene, by using an overhead Time of Flight (ToF) camera. The proposal addresses the problem of robust detection of people, by three means: a carefully designed algorithm to select regions of interest as candidates to belong to people; the generation of a robust feature vector that efficiently model the human upper body; and a people classification stage, to allow robust discrimination of people and other objects in the scene. The proposal also includes a particle filter tracker to allow people identification and tracking. Two classifiers are evaluated, based on Principal Component Analysis (PCA), and Support Vector Machines (SVM). The evaluation is carried out on a subset of a carefully designed dataset with a broad variety of conditions, providing results comparing the PCA and SVM approaches, and also the performance impact of the tracker, with satisfactory results.