In this paper, an algorithm for tracking objects in traffic scenes using a multiple hypothesis approach is presented. The sensor used to get information about the environment is a laser range finder. This sensor has the advantage of obtaining accurate distance and geometric information of the objects in front of the car. In order to achieve a robust classification, geometric information is not always sufficient enough, if objects are separated into multiple parts, due to occlusions or bad segmentation. Therefore the presented method allows several classification results and keeps track of all feasible combinations of disintegrated objects. Thus, it is possible, to process several hypotheses of objects and no objects can be missed.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Object tracking and classification using a multiple hypothesis approach


    Contributors:


    Publication date :

    2004-01-01


    Size :

    544410 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    TPP1.13 Object Tracking and Classification using a Multiple Hypothesis Approach

    Streller, D. / Dietmayer, K. / IEEE | British Library Conference Proceedings | 2004



    Multiple Hypothesis Tracking for Multiple Target Tracking

    Blackman, S.S. | Online Contents | 2004


    An equivalence-class approach to multiple-hypothesis tracking

    Coraluppi, Stefano / Carthel, Craig | IEEE | 2012


    Probability Hypothesis Density Approach for Multi-camera Multi-object Tracking

    Pham, Nam Trung / Huang, Weimin / Ong, S. H. | Springer Verlag | 2007