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    Robust head tracking based on multiple cues fusion in the Kernel-Bayesian framework

    Zhang, X. and Hu, W. and Bao, H. and Maybank, Stephen J. (2013) Robust head tracking based on multiple cues fusion in the Kernel-Bayesian framework. IEEE Transactions on Circuits and Systems for Video Technology 23 (7), pp. 1197-1208. ISSN 1051-8215.

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    This paper presents a robust head tracking algorithm based on multiple cues fusion in a kernel-Bayesian framework. In this algorithm, the object to be tracked is characterized using a spatial-constraint mixture of the Gaussians-based appearance model and a multichannel chamfer matching-based shape model. These two models complement each other and their combination is discriminative in distinguishing the object from the background. A selective updating technique for the appearance model is employed to accommodate appearance and illumination changes. Meantime, the kernel method-mean shift algorithm is embedded into the Bayesian framework to give a heuristic prediction in the hypotheses generation process. This alleviates the great computational load suffered by conventional Bayesian trackers. Experimental results demonstrate that the proposed algorithm is effective.


    Item Type: Article
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Sarah Hall
    Date Deposited: 25 Jul 2013 10:38
    Last Modified: 25 Jul 2013 10:38


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