This paper presents a novel approach to track a nonconvex shape approximation of an extended target based on noisy point measurements. For this purpose, a novel type of random hypersurface model (RHM) called Level-set RHM is introduced that models the interior of a shape with level-sets of an implicit function. Based on the Level-set RHM, a nonlinear measurement equation can be derived that allows to employ a standard Gaussian state estimator for tracking an extended object even in scenarios with moderate measurement noise. In this paper, shapes are described using polygons, and shape regularization is applied using ideas from active contour models.


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    Title :

    Level-set random hypersurface models for tracking nonconvex extended objects


    Contributors:


    Publication date :

    2016-12-01


    Size :

    2233142 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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