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    A Unified 2D-3D Video Scene Change Detection Framework for Mobile Camera Platforms

    152562_152562.pdf (2.887Mb)
    Access Status
    Open access
    Authors
    Leoputra, Wilson
    Tan, Tele
    Venkatesh, Svetha
    Date
    2010
    Type
    Conference Paper
    
    Metadata
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    Citation
    Leoputra, Wilson S. and Tan, Tele and Venkatesh, Svetha. 2010. A Unified 2D-3D Video Scene Change Detection Framework for Mobile Camera Platforms, in Wen, C. (ed), ICARCV 2010, Dec 7 2010, pp. 2147-2153. Singapore: IEEE.
    Source Title
    The 11th International conference on control, automation, robotics and vision
    Source Conference
    ICARCV 2010
    DOI
    10.1109/ICARCV.2010.5707222
    ISBN
    9781424478156
    School
    Department of Computing
    Remarks

    Copyright © 2010 IEEE This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

    URI
    http://hdl.handle.net/20.500.11937/14991
    Collection
    • Curtin Research Publications
    Abstract

    In this paper, we present a novel scene change detection algorithm for mobile camera platforms. Our approachintegrates sparse 3D scene background modelling and dense 2D image background modelling into a unified framework. The 3D scene background modelling identifies inconsistent clusters over time in a set of 3D cloud points as the scene changes. The 2D image background modelling further confirms the scene changes by finding inconsistent appearances in a set of aligned images using the classical MRF background subtraction technique. We evaluate the performance of our proposed system on a number of challenging video datasets obtained from a camera placed on a moving vehicle and the experiments show that our proposed method outperforms previous works in scene change detection, which suggested the feasibility of our approach.

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