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  • A Graph-based Feature Combination Approach to Object Tracking

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    85924_1.pdf (790.8Kb)
    Author(s)
    Quang, Anh Nguyen
    Robles-Kelly, Antonio
    Zhou, Jun
    Griffith University Author(s)
    Zhou, Jun
    Year published
    2010
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    Abstract
    In this paper, we present a feature combination approach to object tracking based upon graph embedding techniques. The method presented here abstracts the low complexity features used for purposes of tracking to a relational structure and employs graph-spectral methods to combine them. This gives rise to a feature combination scheme which minimises the mutual cross-correlation between features and is devoid of free parameters. It also allows an analytical solution making use of matrix factorisation techniques. The new target location is recovered making use of a weighted combination of target-centre shifts corresponding to ...
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    In this paper, we present a feature combination approach to object tracking based upon graph embedding techniques. The method presented here abstracts the low complexity features used for purposes of tracking to a relational structure and employs graph-spectral methods to combine them. This gives rise to a feature combination scheme which minimises the mutual cross-correlation between features and is devoid of free parameters. It also allows an analytical solution making use of matrix factorisation techniques. The new target location is recovered making use of a weighted combination of target-centre shifts corresponding to each of the features under study, where the feature weights arise from a cost function governed by the embedding process. This treatment permits the update of the feature weights in an on-line fashion in a straightforward manner. We illustrate the performance of our method in real-world image sequences and compare our results to a number of alternatives.
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    Conference Title
    COMPUTER VISION - ACCV 2009, PT II
    Volume
    5995
    Issue
    PART 2
    DOI
    https://doi.org/10.1007/978-3-642-12304-7_22
    Copyright Statement
    © 2009 Springer Berlin/Heidelberg. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher.The original publication is available at www.springerlink.com
    Subject
    Computer vision
    Publication URI
    http://hdl.handle.net/10072/51670
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    • Conference outputs

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