This paper proposes a general Kernel-Bayesian framework for object tracking. In this framework, the kernel based method—mean shift algorithm is embedded into the Bayesian framework seamlessly to provide a heuristic prior information to the state transition model, aiming at effectively alleviating the heavy computational load and avoiding sample degeneracy suffered by the conventional Bayesian trackers. Moreover, the tracked object is characterized by a spatial-constraint MOG (Mixture of Gaussians) based appearance model, which is shown more discriminative than the traditional MOG based appearance model. Meantime, a novel selective updating technique for the appearance model is developed to accommodate the changes in both appearance and illumination. Experimental results demonstrate that, compared with Bayesian and kernel based tracking frameworks, the proposed algorithm is more efficient and effective.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Kernel-Bayesian Framework for Object Tracking


    Beteiligte:
    Yagi, Yasushi (Herausgeber:in) / Kang, Sing Bing (Herausgeber:in) / Kweon, In So (Herausgeber:in) / Zha, Hongbin (Herausgeber:in) / Zhang, Xiaoqin (Autor:in) / Hu, Weiming (Autor:in) / Luo, Guan (Autor:in) / Maybank, Steve (Autor:in)

    Kongress:

    Asian Conference on Computer Vision ; 2007 ; Tokyo, Japan November 18, 2007 - November 22, 2007


    Erschienen in:

    Computer Vision – ACCV 2007 ; Kapitel : 78 ; 821-831


    Erscheinungsdatum :

    2007-01-01


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Kernel-based Bayesian filtering for object tracking

    Bohyung Han, / Ying Zhu, / Comaniciu, D. et al. | IEEE | 2005


    Approximate Bayesian methods for kernel-based object tracking

    Zivkovic, Z. / Cemgil, A. T. / Krose, B. | British Library Online Contents | 2009


    Incremental density approximation and kernel-based Bayesian filtering for object tracking

    Bohyung Han, / Comaniciu, D. / Ying Zhu, et al. | IEEE | 2004


    Incremental Density Approximation and Kernel-Based Bayesian Filtering for Object Tracking

    Han, B. / Comaniciu, D. / Zhu, Y. et al. | British Library Conference Proceedings | 2004


    Multiple object tracking with kernel particle filter

    Cheng Chang, / Ansari, R. / Khokhar, A. | IEEE | 2005