In this paper, a probability-based segmentation approach is presented for object tracking. The proposed approach uses the Dirichlet process mixture model to describe the probabilistic distribution of observations in a single scan of a laserscanner. Then the number of segments is inferred from the observations by the Gibbs sampling method. Moreover each segment is classified into one of the three predefined classes such that most of non-vehicle-like objects on the roadsides can be filtered out. Then, the tracking algorithm, called Joint Integrated Probabilistic Data Association Filter (JIPDAF), is applied to track the classified objects and manage existing tracks. Simulations based on real traffic data demonstrate that the non-vehicle-like objects on the roadsides are suppressed. Since the number of objects in the tracking step is decreased, the computation load of the tracking step is decreased.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Object tracking via the probability-based segmentation using laser range images


    Beteiligte:
    Yung-Chou Lee, (Autor:in) / Tesheng Hsiao, (Autor:in)


    Erscheinungsdatum :

    01.06.2010


    Format / Umfang :

    1149559 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Object Tracking Via the Probability-Based Segmentation Using Laser Range Images, pp. 197-202

    Hsiao, T. / Lee, Y.-C. / Institute of Electrical and Electronics Engineers | British Library Conference Proceedings | 2010


    VEHICLE AND OBJECT MODELS FOR ROBUST TRACKING IN TRAFFIC SCENES USING LASER RANGE IMAGES

    Streller, D. / Fuerstenberg, K. / Dietmayer, K. et al. | British Library Conference Proceedings | 2002


    Vehicle and object models for robust tracking in traffic scenes using laser range images

    Streller, D. / Furstenberg, K. / Dietmayer, K. | IEEE | 2002


    Multi-Object Tracking, Segmentation and Validation in Thermal Images

    Muresan, Mircea Paul / Danescu, Radu / Nedevschi, Sergiu | IEEE | 2023


    Posterior Probability Object Tracking Method Using Momentum Based Level Set

    Haocheng Le, / Linglong Hu, / Yuanjing Feng, | IEEE | 2010