The theory of embedded time series is shown applicable for determining a reasonable lower bound on the length of test sequence required for accurate classification of moving objects. Sequentially recorded feature vectors of a moving object form a training trajectory in feature space. Each of the sequences of feature vector components is a time series, and under certain conditions, each of these time series has approximately the same fractal dimension. The embedding theorem may be applied to this fractal dimension to establish a sufficient number of observations to determine the feature space trajectory of the object. It is argued that this number is a reasonable lower bound on test sequence length for use in object classification. Experiments with data corresponding to five military vehicles (observed following a projected Lorenz trajectory on a viewing sphere) show that this bound is indeed adequate.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    An application of embedology to spatio-temporal pattern recognition


    Contributors:
    Stright, J.R. (author) / Rogers, S.K. (author) / Quinn, D.W. (author) / Fielding, K.H. (author)


    Publication date :

    1996-04-01


    Size :

    3487745 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English