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International Symposium on Visual Computing

ISVC 2015: Advances in Visual Computing pp 210–220Cite as

An Efficient Non-parametric Background Modeling Technique with CUDA Heterogeneous Parallel Architecture

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9474))

Abstract

Foreground detection plays an important role in many content based video processing applications. To detect moving objects in a scene, the changes inherent to the background need to be modelled. In this work we propose a non-parametric statistical background modeling technique. Moreover, the proposed modeling framework is designed to utilize Nvidia’s CUDA architecture to accelerate the overall foreground detection process. We present three main contributions: (1) a novelty detection mechanism capable of building accurate statistical models for background pixels; (2) an adaptive mechanism for classifying pixels based on their respective statistical background model; and (3) the complete implementation of the proposed approach based on the Nvidia’s CUDA architecture. Comparisons and both qualitative and quantitative experimental results show that the proposed work achieves considerable accuracy in detecting foreground objects, while reaching orders of magnitude speed-up compared to traditional approaches.

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Acknowledgements

This material is based upon work supported in part by the U. S. Army Research Laboratory and the U. S. Department of Defense under grant number W911NF-15-1-0024 and W911NF-15-1-0455, and by the NASA MUREP ASTAR program under grant number NNX15AU31H. This support does not necessarily imply endorsement by the DoD or NASA.

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Authors and Affiliations

  1. Computation and Advanced Visualization Engineering Lab, University of Houston–Victoria, Victoria, TX, USA

    Brandon Wilson & Alireza Tavakkoli

Authors
  1. Brandon Wilson
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  2. Alireza Tavakkoli
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Corresponding author

Correspondence to Brandon Wilson .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. University of Houston, Houston, Texas, USA

    Ioannis Pavlidis

  6. IBM T.J. Watson Research Center, Yorktown Heights, New York, USA

    Rogerio Feris

  7. Purdue University, West Lafayette, Indiana, USA

    Tim McGraw

  8. Side Effects Software, Santa Monica, California, USA

    Mark Elendt

  9. The DiVE, Durham, North Carolina, USA

    Regis Kopper

  10. Texas A&M University, College Station, Texas, USA

    Eric Ragan

  11. Kent State University, Kent, Ohio, USA

    Zhao Ye

  12. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Gunther Weber

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© 2015 Springer International Publishing Switzerland

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Wilson, B., Tavakkoli, A. (2015). An Efficient Non-parametric Background Modeling Technique with CUDA Heterogeneous Parallel Architecture. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9474. Springer, Cham. https://doi.org/10.1007/978-3-319-27857-5_19

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  • DOI: https://doi.org/10.1007/978-3-319-27857-5_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27856-8

  • Online ISBN: 978-3-319-27857-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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