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    Human activity monitoring for falling detection. A realistic framework.

    TítuloHuman activity monitoring for falling detection. A realistic framework.
    Tipo de publicaciónConference Paper
    Año de publicación2016
    AutoresBaptista, M, Martínez, C, Losada-Gutiérrez, C, Marron, M
    Idioma de publicaciónEnglish
    Conference Name7th International conference on Indoor Positioning and Indoor Navigation (IPIN 2016)
    Fecha de publicación09/2016
    Numero ISBN978-1-5090-2424-7

    During the last decades topics such as video analysis
    and image understanding techniques have experimented
    an important evolution due to its inclusion in applications
    such as surveillance, intelligent spaces and assisted living. In
    order to validate all related works different datasets have been
    distributed within the research community: CAVIAR, KTH,
    Weizmann, INRIA or MuHAVI are some of the most well-known
    examples, but in most cases these datasets have not been created
    neither specifically for the mentioned applications, nor in realistic
    scenarios. Within this context, in this paper we present a work
    that implements a solution for falling detection from monocular
    video sequences acquired with an standard video-camera. It
    includes, both the multi-person detector and tracker in realistic
    scenarios, and the action classifier for each of the detected
    persons. Besides, it is also presented a newly created dataset with
    realistic scenes specifically designed for surveillance applications.
    Scientific soundness and development of the proposed algorithm
    and its results and validation, both within well-known datasets
    as CAVIAR and KTH and within the one ad-hoc generated for
    the applications of interest, are discussed in the paper.

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