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Robust Crowd Segmentation and Counting in Indoor Scenes

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MultiMedia Modeling (MMM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9516))

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Abstract

This paper proposes a fast counting approach to estimate the number of people in indoor scenes. Firstly, a pre-processing step is used. In order to obtain a robust gray image in complex light conditions this step includes color correlation, image smoothing and contrast stretch. Secondly, we extract foreground region by background edge modeling and contour filling. Finally, after a foreground normalization based on camera calibration, we obtain the counting results with template matching. Experimental results show that compared with the Bayesian counting approach [2], our approach is robust to illumination variation and achieves a real-time counting result in indoor scenes.

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Authors and Affiliations

  1. School of Automation, Wuhan University of Technology, Wuhan, 430070, China

    Ren Yang & Huazhong Xu

  2. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Jinqiao Wang

Authors
  1. Ren Yang
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  2. Huazhong Xu
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  3. Jinqiao Wang
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Corresponding author

Correspondence to Ren Yang .

Editor information

Editors and Affiliations

  1. University of Texas at San Antonio, San Antonio, USA

    Qi Tian

  2. Dept. of Information Engineering, University of Trento, Povo, Trento, Italy

    Nicu Sebe

  3. EECS, University of Central Florida, Orlando, Florida, USA

    Guo-Jun Qi

  4. EURECOM, Sophia-Antipolis, France

    Benoit Huet

  5. Hefei University of Technology, Hefei, Anhui, China

    Richang Hong

  6. School of Computing and Information, Hefei University of Technology, Hefei, Anhui, China

    Xueliang Liu

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© 2016 Springer International Publishing Switzerland

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Yang, R., Xu, H., Wang, J. (2016). Robust Crowd Segmentation and Counting in Indoor Scenes. In: Tian, Q., Sebe, N., Qi, GJ., Huet, B., Hong, R., Liu, X. (eds) MultiMedia Modeling. MMM 2016. Lecture Notes in Computer Science(), vol 9516. Springer, Cham. https://doi.org/10.1007/978-3-319-27671-7_42

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  • DOI: https://doi.org/10.1007/978-3-319-27671-7_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27670-0

  • Online ISBN: 978-3-319-27671-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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