Visual gesture recognition is one of the main areas of research in human-computer and human-robot interfaces. In this paper we present a novel visual gesture recognition method for aircraft marshalling signals. To capture hand motion information, we used a color-based tracking algorithm with an adaptive window for each frame. A feature selection algorithm was used to classify the motion information into four different gesture phases. By using the gesture phase information, we built the gesture model, which consisted of a symbol sequence and a number of probabilities. Each gesture model was learned from the longest common subsequence (LCS) of a set of symbol sequences and the probability of the symbols. A similarity measure using the proposed gesture model is presented that combines the deterministic and probabilistic matching scores. In the experiments we show the efficiency and accuracy of the proposed method.


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    Titel :

    Visual recognition of aircraft marshalling signals using gesture phase analysis


    Beteiligte:
    Choi, Cheolmin (Autor:in) / Ahn, Jung-Ho (Autor:in) / Byun, Hyeran (Autor:in)


    Erscheinungsdatum :

    01.06.2008


    Format / Umfang :

    339738 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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