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    Viewpoint Distortion Compensation in Practical Surveillance Systems

    Access Status
    Fulltext not available
    Authors
    Arandjelovic, O.
    Pham, DucSon
    Venkatesh, S.
    Date
    2015
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Arandjelovic, O. and Pham, D. and Venkatesh, S. 2015. Viewpoint Distortion Compensation in Practical Surveillance Systems, in Enrico Magli, Stefano Tubaro, Anthony Vetro (ed), 2015 IEEE International Conference on Multimedia & Expo (ICME) , Jun 29 2015. Torino, Italy: IEEE.
    Source Title
    Proceedings of the 2015 IEEE International Conference on Multimedia & Expo (ICME)
    Source Conference
    2015 IEEE International Conference on Multimedia & Expo (ICME)
    Additional URLs
    http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7160935
    ISBN
    9781479970827
    School
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/21572
    Collection
    • Curtin Research Publications
    Abstract

    Our aim is to estimate the perspective-effected geometric distortion of a scene from a video feed. In contrast to all previous work we wish to achieve this using from low-level, spatio-temporally local motion features used in commercial semi-automatic surveillance systems. We: (i) describe a dense algorithm which uses motion features to estimate the perspective distortion at each image locus and then polls all such local estimates to arrive at the globally best estimate, (ii) present an alternative coarse algorithm which subdivides the image frame into blocks, and uses motion features to derive block-specific motion characteristics and constrain the relationships between these characteristics, with the perspective estimate emerging as a result of a global optimization scheme, and (iii) report the results of an evaluation using nine large sets acquired using existing close-circuit television (CCTV) cameras. Our findings demonstrate that both of the proposed methods are successful, their accuracy matching that of human labelling using complete visual data.

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