Abstract A new approach is presented to vehicle-class recognition from video clips. Two new concepts introduced are: probes consisting of local 3-d curve-groups which when projected into video frames are features for recognizing vehicle classes in video clips; and Bayesian recognition based on class probability densities for groups of 3-d distances between pairs of 3-d probes. A full Bayesian recognizer is realized via Monte Carlo simulation method. Also, a sub-optimal but robust camera calibration method is employed and tested extensively.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Bayesian vehicle class recognition using 3-d probe


    Beteiligte:
    Han, D. (Autor:in) / Cooper, D. B. (Autor:in) / Hahn, H. -S. (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2013-09-20


    Format / Umfang :

    10 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Bayesian vehicle class recognition using 3-d probe

    Han, D. / Cooper, D. B. / Hahn, H. S. | British Library Online Contents | 2013


    Bayesian vehicle class recognition using 3-d probe

    Han, D. / Cooper, D. B. / Hahn, H. -S. | Online Contents | 2013


    Vehicle Class Recognition Using Multiple Video Cameras

    Han, D. / Hwang, J. / Hahn, H.-s. et al. | British Library Conference Proceedings | 2011



    Vehicle sparse recognition via class dictionary learning

    Ji-xin Liu, / Ning Sun, / Guang Han, et al. | IEEE | 2017