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    Incremental learning of weighted tensor subspace for visual tracking

    Wen, J. and Li, Xuelong and Gao, X. and Tao, D. (2009) Incremental learning of weighted tensor subspace for visual tracking. In: UNSPECIFIED (ed.) International Conference on Systems, Man and Cybernetics. New York, USA: Institute of Electrical and Electronics Engineers, pp. 3688-3693. ISBN 9781424427932.

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    Tensor analysis has been widely utilized in image-related machine learning applications, which has preferable performance over the vector-based approaches for its capability of holding the spatial structure information in some research field. The traditional tensor representation only includes the intensity values, which is sensitive to illumination variation. For this purpose, a weighted tensor subspace (WTS) is defined as object descriptor by combining the Retinex image with the original image. Then, an incremental learning algorithm is developed for WTS to adapt to the appearance change during the tracking. The proposed method could learn the lightness changing incrementally and get robust tracking performance under various luminance conditions. The experimental results illustrate the effectiveness of the proposed visual tracking scheme.


    Item Type: Book Section
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Depositing User: Sarah Hall
    Date Deposited: 11 Jul 2013 16:03
    Last Modified: 09 Aug 2023 12:33


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