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Variational Inference for Background Subtraction in Infrared Imagery

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Advances in Visual Computing (ISVC 2015)

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

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Abstract

We propose a Gaussian mixture model with fixed but unknown number of components for background subtraction in infrared imagery. Following a Bayesian approach, our method automatically estimates the number of components as well as their parameters, while simultaneously it avoids over/under fitting. The equations for estimating model parameters are analytically derived and thus our method does not require any sampling algorithm that is computationally and memory inefficient. The pixel density estimate is followed by an efficient and highly accurate updating mechanism, which permits our system to be automatically adapted to dynamically changing visual conditions. Experimental results and comparisons with other methods indicate the high potential of the proposed method while keeping computational cost suitable for real-time applications.

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Acknowledgements

This research was funded from European Unions FP7 under grant agreement n.313161, eVACUATE Project (www.evacuate.eu).

Author information

Authors and Affiliations

  1. Technical University of Crete, University Campus, Kounoupidiana, 73100, Chania, Greece

    Konstantinos Makantasis

  2. National Technical University of Athens, Zografou Campus, 15780, Athens, Greece

    Anastasios Doulamis

  3. Institute of Communication and Computer Systems, Athens, Greece

    Konstantinos Loupos

Authors
  1. Konstantinos Makantasis
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  2. Anastasios Doulamis
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  3. Konstantinos Loupos
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Corresponding author

Correspondence to Konstantinos Makantasis .

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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Makantasis, K., Doulamis, A., Loupos, K. (2015). Variational Inference for Background Subtraction in Infrared Imagery. 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_62

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  • DOI: https://doi.org/10.1007/978-3-319-27857-5_62

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  • 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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