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  • MRF-based Background Initialisation for Improved Foreground Detection in Cluttered Surveillance Videos

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    Sanderson437188-Accepted.pdf (934.1Kb)
    Author(s)
    Reddy, Vikas
    Sanderson, Conrad
    Sanin, Andres
    Lovell, Brian
    Griffith University Author(s)
    Sanderson, Conrad
    Year published
    2010
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    Abstract
    Robust foreground object segmentation via background modelling is a difficult problem in cluttered environments, where obtaining a clear view of the background to model is almost impossible. In this paper, we propose a method capable of robustly estimating the background and detecting regions of interest in such environments. In particular, we propose to extend the background initialisation component of a recent patch-based foreground detection algorithm with an elaborate technique based on Markov Random Fields, where the optimal labelling solution is computed using iterated conditional modes. Rather than relying purely on ...
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    Robust foreground object segmentation via background modelling is a difficult problem in cluttered environments, where obtaining a clear view of the background to model is almost impossible. In this paper, we propose a method capable of robustly estimating the background and detecting regions of interest in such environments. In particular, we propose to extend the background initialisation component of a recent patch-based foreground detection algorithm with an elaborate technique based on Markov Random Fields, where the optimal labelling solution is computed using iterated conditional modes. Rather than relying purely on local temporal statistics, the proposed technique takes into account the spatial continuity of the entire background. Experiments with several tracking algorithms on the CAVIAR dataset indicate that the proposed method leads to considerable improvements in object tracking accuracy, when compared to methods based on Gaussian mixture models and feature histograms.
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    Conference Title
    Lecture Notes in Computer Science
    Volume
    6494
    DOI
    https://doi.org/10.1007/978-3-642-19318-7_43
    Copyright Statement
    © Springer-Verlag Berlin Heidelberg 2011. 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
    Artificial Intelligence and Image Processing
    Computer Vision
    Knowledge Representation and Machine Learning
    Publication URI
    http://hdl.handle.net/10072/401038
    Collection
    • Conference outputs

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