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Is a Memoryless Motion Detection Truly Relevant for Background Generation with LaBGen?

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Advanced Concepts for Intelligent Vision Systems (ACIVS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10617))

Abstract

The stationary background generation problem consists in generating a unique image representing the stationary background of a given video sequence. The LaBGen background generation method combines a pixel-wise median filter and a patch selection mechanism based on a motion detection performed by a background subtraction algorithm. In our previous works related to LaBGen, we have shown that, surprisingly, the frame difference algorithm provides the most effective motion detection on average. Compared to other background subtraction algorithms, it detects motion between two frames without relying on additional past frames, and is therefore memoryless. In this paper, we experimentally check whether the memoryless property is truly relevant for LaBGen, and whether the effective motion detection provided by the frame difference is not an isolated case. For this purpose, we introduce LaBGen-OF, a variant of LaBGen leverages memoryless dense optical flow algorithms for motion detection. Our experiments show that using a memoryless motion detector is an adequate choice for our background generation framework, and that LaBGen-OF outperforms LaBGen on the SBMnet dataset. We further provide an open-source C++ implementation of both methods at http://www.telecom.ulg.ac.be/labgen.

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Notes

  1. 1.

    http://pione.dinf.usherbrooke.ca/sbmc2016.

  2. 2.

    LaBGen, with the set of parameters proposed in [9], runs at a frame rate of more than 1300 fps for a VGA video sequence on a Core i7-4790K.

  3. 3.

    http://sbmi2015.na.icar.cnr.it/SBIdataset.html.

  4. 4.

    http://scenebackgroundmodeling.net.

  5. 5.

    http://opencv.org.

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Acknowledgments

We are thankful to Pr. Pierre-Marc Jodoin for letting us use the SBMnet web platform to assess some variants of LaBGen.

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Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, Montefiore Institute, University of Liège, Liège, Belgium

    Benjamin Laugraud & Marc Van Droogenbroeck

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  1. Benjamin Laugraud
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  2. Marc Van Droogenbroeck
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Corresponding author

Correspondence to Benjamin Laugraud .

Editor information

Editors and Affiliations

  1. DGA, Paris, France

    Jacques Blanc-Talon

  2. University of Antwerp, Antwerp, Belgium

    Rudi Penne

  3. Ghent University - imec, Ghent, Belgium

    Wilfried Philips

  4. CSIRO Data 61, Canberra, Aust Capital Terr, Australia

    Dan Popescu

  5. University of Antwerp, Wilrijk, Belgium

    Paul Scheunders

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Laugraud, B., Van Droogenbroeck, M. (2017). Is a Memoryless Motion Detection Truly Relevant for Background Generation with LaBGen?. In: Blanc-Talon, J., Penne, R., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2017. Lecture Notes in Computer Science(), vol 10617. Springer, Cham. https://doi.org/10.1007/978-3-319-70353-4_38

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  • DOI: https://doi.org/10.1007/978-3-319-70353-4_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70352-7

  • Online ISBN: 978-3-319-70353-4

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