In this paper we present a stereovision-based approach for tracking multiple objects in crowded environments where, typically, the road lane markings are not visible and the surrounding infrastructure is not known. The proposed technique relies on measurement data provided by an intermediate occupancy grid derived from processing a stereovision-based elevation map and on free-form object delimiters extracted from this grid. Unlike other existing methods that track rigid objects using also rigid representations, we present a particle filter-based solution for tracking visual appearance-based free-form obstacle representations. At each step, the particle state is described by two components, i.e., the object's dynamic parameters and its estimated geometry. In order to solve the high-dimensionality state–space problem, a Rao–Blackwellized particle filter is used. By accurately modeling the object geometry using the polygonal lines instead of a 3-D box and, at the same time, separating the position and speed tracking from the geometry tracking at the estimator level, the proposed solution combines the efficiency of the rigid model with the benefits of a flexible object model.


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    Title :

    Stereovision-Based Multiple Object Tracking in Traffic Scenarios Using Free-Form Obstacle Delimiters and Particle Filters


    Contributors:


    Publication date :

    2015-02-01


    Size :

    2743265 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    MODELING UNSTRUCTURED ENVIRONMENTS WITH DYNAMIC PERSISTENCE GRIDS AND OBJECT DELIMITERS IN URBAN TRAFFIC SCENARIOS

    Vatavu, A. / Nedevschi, S. / Institute of Electrical and Electronics Engineers | British Library Conference Proceedings | 2013



    Obstacle Detection in Urban Traffic Using Stereovision

    Huang, Y. / IEEE | British Library Conference Proceedings | 2005