Kernel-based objective functions optimized using the mean shift algorithm have been demonstrated as an effective means of tracking in video sequences. The resulting algorithms combine the robustness and invariance properties afforded by traditional density-based measures of image similarity, while connecting these techniques to continuous optimization algorithms. This paper demonstrates a connection between kernel-based algorithms and more traditional template tracking methods. here is a well known equivalence between the kernel-based objective function and an SSD-like measure on kernel-modulated histograms. It is shown that under suitable conditions, the SSD-like measure can be optimized using Newton-style iterations. This method of optimization is more efficient (requires fewer steps to converge) than mean shift and makes fewer assumptions on the form of the underlying kernel structure. In addition, the methods naturally extend to objective functions optimizing more elaborate parametric motion models based on multiple spatially distributed kernels. We demonstrate multi-kernel methods on a variety of examples ranging from tracking of unstructured objects in image sequences to stereo tracking of structured objects to compute full 3D spatial location.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Multiple kernel tracking with SSD


    Contributors:
    Hager, G.D. (author) / Dewan, M. (author) / Stewart, C.V. (author)


    Publication date :

    2004-01-01


    Size :

    565564 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Multiple Kernel Tracking with SSD

    Hager, G. / Dewan, M. / Stewart, C. et al. | British Library Conference Proceedings | 2004


    Multiple collaborative kernel tracking

    Zhimin Fan / Ying Wu / Ming Yang | IEEE | 2005


    Multiple object tracking with kernel particle filter

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


    Occlusion handling approach in visual tracking based on multiple-kernel fusion

    Peng, X. / Miyi, D. / Qi, Z. | British Library Online Contents | 2012


    Kernel-Bayesian Framework for Object Tracking

    Zhang, Xiaoqin / Hu, Weiming / Luo, Guan et al. | Springer Verlag | 2007