ISSN 0253-2778

CN 34-1054/N

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A background subtraction algorithm based on biological vision characteristics

  • 1.

    Department of Automation,University of Science and Technology of China,Hefei 230027,China

  • 2.

    Institute of Advanced Manufacturing Technology, Hefei Institute of Physical Science, Chinese Academy of Science, Hefei 230031

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2014.04.003
  • Received Date: 06 August 2013
  • Accepted Date: 04 September 2013
  • Rev Recd Date: 04 September 2013
  • Publish Date: 30 April 2014
    Abstract

  • Abstract

    For the problem of how to build a robust background model and update the background model, a background subtraction algorithm based on biological vision characteristics combined with the ViBe algorithm was proposed. Firstly, utilizing the extrinsic nearsightedness characteristic of the frogs visual system, the meaning of a pretreatment method called “region fuzzy” and its implementation were described. Then, considering the characteristics of color cognition by humans, a measurement criterion for color difference based on Webers law in the LUV color space was given. Finally, specific implementations of the algorithm were introduced from three main aspects: Background modeling, foreground detection and background model updating. Experimental results show that this algorithm can improve the accuracy of moving object detection.

    Abstract

    For the problem of how to build a robust background model and update the background model, a background subtraction algorithm based on biological vision characteristics combined with the ViBe algorithm was proposed. Firstly, utilizing the extrinsic nearsightedness characteristic of the frogs visual system, the meaning of a pretreatment method called “region fuzzy” and its implementation were described. Then, considering the characteristics of color cognition by humans, a measurement criterion for color difference based on Webers law in the LUV color space was given. Finally, specific implementations of the algorithm were introduced from three main aspects: Background modeling, foreground detection and background model updating. Experimental results show that this algorithm can improve the accuracy of moving object detection.
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    • References(26)
  • [1]
    Wren C R, Azarbayejani A, Darrell T, et al. Pfinder: Real-time tracking of the human body[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 780-785.
    [2]
    Stauffer C, Grimson W E L. Adaptive background mixture models for real-time tracking[C]// Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Fort Collins, USA: IEEE Press, 1999, 2: 252-258.
    [3]
    Jepson A D, Fleet D J, El-Maraghi T F. Robust online appearance models for visual tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(10): 1 296-1 311.
    [4]
    Zivkovic Z. Improved adaptive Gaussian mixture model for background subtraction[C]// Proceedings of the 17th International Conference on Pattern Recognition. Cambridge, UK: IEEE Press, 2004, 2: 28-31.
    [5]
    Elgammal A, Harwood D, Davis L. Nonparametric model for background subtraction[C]//Proceedings of the 6th European Conference on Computer Vision. Dublin, Ireland, 2000: 751-767.
    [6]
    Sheikh Y, Shah M. Bayesian modeling of dynamic scenes for object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(11): 1 778-1 792.
    [7]
    Kim K, Chalidabhongse T H, Harwood D, et al. Real-time foreground-background segmentation using codebook model[J]. Real-time imaging, 2005, 11(3): 172-185.
    [8]
    Barnich O, Van Droogenbroeck M. ViBe: A universal background subtraction algorithm for video sequences[J]. IEEE Transactions on Image Processing, 2011, 20(6): 1 709-1 724.
    [9]
    Maddalena L, Petrosino A. A self-organizing approach to background subtraction for visual surveillance applications[J]. IEEE Transactions on Image Processing, 2008, 17(7): 1 168-1 177.
    [10]
    Maddalena L, Petrosino A. The SOBS algorithm: What are the limits?[C]// Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Providence, USA: IEEE Press, 2012: 21-26.
    [11]
    Li L Y, Huang W M, Gu I Y H, et al. Statistical modeling of complex backgrounds for foreground object detection[J]. IEEE Transactions on Image Processing, 2004, 13(11): 1 459-1 472.
    [12]
    Heikkil M, Pietikinen M. A texture-based method for modeling the background and detecting moving objects[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(4): 657-662.
    [13]
    Liao S C, Zhao G Y, Kellokumpu V, et al. Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes[C]//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, USA: IEEE Press, 2010: 1 301-1 306.
    [14]
    Parag T, Elgammal A, Mittal A. A framework for feature selection for background subtraction[C]// Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE Press, 2006, 2: 1 916-1 923.
    [15]
    Han B, Davis L S. Density-based multifeature background subtraction with support vector machine[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(5): 1 017-1 023.
    [16]
    Guo J M, Hsu C S. Cascaded background subtraction using block-based and pixel-based codebooks[C]// Proceedings of the 20th IEEE International Conference on Pattern Recognition. Istanbul, Turkey: IEEE Press, 2010: 1 373-1 376.
    [17]
    Zaharescu A, Jamieson M. Multi-scale multi-feature codebook-based background subtraction[C]// Proceedings of the IEEE International Conference on Computer Vision Workshops. Barcelona, Spain: IEEE Press, 2011: 1 753-1 760.
    [18]
    Lettvin J Y, Maturana H R, McCulloch W S, et al. What the frogs eye tells the frogs brain[J]. Proceedings of the IRE, 1959, 47(11): 1 940-1 951.
    [19]
    Ingle D. Disinhibition of tectal neurons by pretectal lesions in the frog[J]. Science, 1973, 180(4084): 422-424.
    [20]
    Wang Zhiling, Chen Zonghai, Xu Xiaoxiao, et al. A fuzzy region understanding tactic for object tracking based on frogs vision characteristic[J]. Acta Automatica Sinica, 2009, 35(8): 1 048-1 054.
    王智灵, 陈宗海, 徐萧萧, 等. 基于蛙眼视觉特性的运动目标模糊化区域理解跟踪方法[J]. 自动化学报, 2009, 35(8): 1 048-1 054.
    [21]
    Comaniciu D, Meer P. Mean shift: A robust approach toward feature space analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(5): 603-619.
    [22]
    Jain A K. Fundamentals of Digital Image Processing[M]. Englewood Cliffs, USA: Prentice Hall, 1989.
    [23]
    Sharma G, Trussell H.J. Digital color imaging[J]. IEEE Transactions on Image Processing, 1997, 6(7): 901-932.
    [24]
    Goyette N, Jodoin P, Porikli F, et al. Changedetection.net: A new change detection benchmark dataset[C]// Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Providence, USA: IEEE Press, 2012: 1-8.
    [25]
    Hofmann M, Tiefenbacher P, Rigoll G. Background segmentation with feedback: The pixel-based adaptive segmenter[C]// Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Providence, USA: IEEE Press, 2012: 38-43.
    [26]
    Van Droogenbroeck M, Paquot O. Background subtraction: Experiments and improvements for ViBe[C]//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Providence, USA: IEEE Press, 2012: 32-37.
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    [1]
    Wren C R, Azarbayejani A, Darrell T, et al. Pfinder: Real-time tracking of the human body[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 780-785.
    [2]
    Stauffer C, Grimson W E L. Adaptive background mixture models for real-time tracking[C]// Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Fort Collins, USA: IEEE Press, 1999, 2: 252-258.
    [3]
    Jepson A D, Fleet D J, El-Maraghi T F. Robust online appearance models for visual tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(10): 1 296-1 311.
    [4]
    Zivkovic Z. Improved adaptive Gaussian mixture model for background subtraction[C]// Proceedings of the 17th International Conference on Pattern Recognition. Cambridge, UK: IEEE Press, 2004, 2: 28-31.
    [5]
    Elgammal A, Harwood D, Davis L. Nonparametric model for background subtraction[C]//Proceedings of the 6th European Conference on Computer Vision. Dublin, Ireland, 2000: 751-767.
    [6]
    Sheikh Y, Shah M. Bayesian modeling of dynamic scenes for object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(11): 1 778-1 792.
    [7]
    Kim K, Chalidabhongse T H, Harwood D, et al. Real-time foreground-background segmentation using codebook model[J]. Real-time imaging, 2005, 11(3): 172-185.
    [8]
    Barnich O, Van Droogenbroeck M. ViBe: A universal background subtraction algorithm for video sequences[J]. IEEE Transactions on Image Processing, 2011, 20(6): 1 709-1 724.
    [9]
    Maddalena L, Petrosino A. A self-organizing approach to background subtraction for visual surveillance applications[J]. IEEE Transactions on Image Processing, 2008, 17(7): 1 168-1 177.
    [10]
    Maddalena L, Petrosino A. The SOBS algorithm: What are the limits?[C]// Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Providence, USA: IEEE Press, 2012: 21-26.
    [11]
    Li L Y, Huang W M, Gu I Y H, et al. Statistical modeling of complex backgrounds for foreground object detection[J]. IEEE Transactions on Image Processing, 2004, 13(11): 1 459-1 472.
    [12]
    Heikkil M, Pietikinen M. A texture-based method for modeling the background and detecting moving objects[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(4): 657-662.
    [13]
    Liao S C, Zhao G Y, Kellokumpu V, et al. Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes[C]//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, USA: IEEE Press, 2010: 1 301-1 306.
    [14]
    Parag T, Elgammal A, Mittal A. A framework for feature selection for background subtraction[C]// Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE Press, 2006, 2: 1 916-1 923.
    [15]
    Han B, Davis L S. Density-based multifeature background subtraction with support vector machine[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(5): 1 017-1 023.
    [16]
    Guo J M, Hsu C S. Cascaded background subtraction using block-based and pixel-based codebooks[C]// Proceedings of the 20th IEEE International Conference on Pattern Recognition. Istanbul, Turkey: IEEE Press, 2010: 1 373-1 376.
    [17]
    Zaharescu A, Jamieson M. Multi-scale multi-feature codebook-based background subtraction[C]// Proceedings of the IEEE International Conference on Computer Vision Workshops. Barcelona, Spain: IEEE Press, 2011: 1 753-1 760.
    [18]
    Lettvin J Y, Maturana H R, McCulloch W S, et al. What the frogs eye tells the frogs brain[J]. Proceedings of the IRE, 1959, 47(11): 1 940-1 951.
    [19]
    Ingle D. Disinhibition of tectal neurons by pretectal lesions in the frog[J]. Science, 1973, 180(4084): 422-424.
    [20]
    Wang Zhiling, Chen Zonghai, Xu Xiaoxiao, et al. A fuzzy region understanding tactic for object tracking based on frogs vision characteristic[J]. Acta Automatica Sinica, 2009, 35(8): 1 048-1 054.
    王智灵, 陈宗海, 徐萧萧, 等. 基于蛙眼视觉特性的运动目标模糊化区域理解跟踪方法[J]. 自动化学报, 2009, 35(8): 1 048-1 054.
    [21]
    Comaniciu D, Meer P. Mean shift: A robust approach toward feature space analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(5): 603-619.
    [22]
    Jain A K. Fundamentals of Digital Image Processing[M]. Englewood Cliffs, USA: Prentice Hall, 1989.
    [23]
    Sharma G, Trussell H.J. Digital color imaging[J]. IEEE Transactions on Image Processing, 1997, 6(7): 901-932.
    [24]
    Goyette N, Jodoin P, Porikli F, et al. Changedetection.net: A new change detection benchmark dataset[C]// Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Providence, USA: IEEE Press, 2012: 1-8.
    [25]
    Hofmann M, Tiefenbacher P, Rigoll G. Background segmentation with feedback: The pixel-based adaptive segmenter[C]// Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Providence, USA: IEEE Press, 2012: 38-43.
    [26]
    Van Droogenbroeck M, Paquot O. Background subtraction: Experiments and improvements for ViBe[C]//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Providence, USA: IEEE Press, 2012: 32-37.
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