Skip to main content
SpringerLink
Log in

Context-Awareness Based Adaptive Gaussian Mixture Background Modeling

  • Conference paper
  • First Online:
Image and Video Technology (PSIVT 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10799))

Included in the following conference series:

  • 1081 Accesses

Abstract

Classical Gaussian mixture model (GMM) can represent the multi-states of a single pixel. GMM is robust when dealing with complex scenes with gradual-changed illumination. However, it still leads to false detection because of the change of pixel values in the same position when the background scenes get revealed after being covered. In this paper, a Context-Awareness based Gaussian mixture model (CAGMM) is proposed to tag the Gaussian model which used to be background. Experimental results show that the proposed CAGMM can remember scenes and adapt to the change of scenes more quickly, thus the false detection rate is reduced.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Gamboa-Aispuro, J.M., Aguilar-Ponce, R.M., Tecpanecatl-Xihuitl, J.L.: Background subtraction based on mutual information. In: IEEE International Autumn Meeting on Power, Electronics and Computing. Institute of Electrical and Electronics Engineers Inc., Mexico (2016)

    Google Scholar 

  2. Ge, W., et al.: Dynamic background estimation and complementary learning for pixel-wise foreground/background segmentation. Pattern Recogn. 59, 112–125 (2016)

    Article  Google Scholar 

  3. Dong, Y., Desouza, G.N.: Adaptive learning of multi-subspace for foreground detection under illumination changes. Comput. Vis. Image Underst. 115, 31–49 (2011)

    Article  Google Scholar 

  4. Stauffer C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, Fort Collins, CO (1999)

    Google Scholar 

  5. Wang, K., Liang, Y., Xing, X., Zhang, R.: Target detection algorithm based on gaussian mixture background subtraction model. In: Deng, Z., Li, H. (eds.) Proceedings of the 2015 Chinese Intelligent Automation Conference. LNEE, vol. 336, pp. 439–447. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-46469-4_47

    Chapter  Google Scholar 

  6. Kim, W., Kim, C.: Background subtraction for dynamic texture scenes using fuzzy color histograms. IEEE Sig. Process. Lett. 19(3), 127–130 (2012)

    Article  Google Scholar 

  7. Wang, Z., Zhang, L., Bao, H.: Adaptive background model based on hybrid structure neural network. Acta Electronica Sinica 39(5), 1053–1058 (2011)

    Google Scholar 

  8. Han, B., Davis, L.S.: Density-based multifeature background subtraction with support vector machine. IEEE Trans. Pattern Anal. Mach. Intell. 34(5), 1017–1023 (2012)

    Article  Google Scholar 

  9. Lin, H.-H., Chuang, J.-H., Liu, T.-L.: Regularized background adaptation: a novel learning rate control scheme for gaussian mixture modeling. IEEE Trans. Image Process. 20(3), 822–836 (2011)

    Article  MathSciNet  Google Scholar 

  10. Zivkovic, Z.: Improved adaptive Gaussian mixture model for background subtraction. In: IEEE International Conference on Pattern Recognition, pp. 28–31. IEEE (2004)

    Google Scholar 

  11. Cheng, F.-C., Huang, S.-C., Ruan, S.-J.: Illumination-sensitive background modeling approach for accurate moving object detection. IEEE Trans. Broadcast. 57(4), 794–801 (2011)

    Article  Google Scholar 

  12. Zuo, J., Liang, Y., Zhao, C., et al.: Gaussian mixture background model based on entropy image and membership-degree-image. J. Electron. Inf. Technol. 30(8), 1918–1922 (2008)

    Article  Google Scholar 

  13. Qi, Y.-J., Wang, Y.-J., Li, Y.-P.: Memory-based Gaussian mixture background modeling. Acta Automatica Sinica 36(11), 1520–1526 (2010)

    Article  MathSciNet  Google Scholar 

  14. Xiao, J., et al.: Hierarchical tone mapping based on image colour appearance model. IET Comput. Vis. 8(4), 358–364 (2014)

    Article  Google Scholar 

  15. Wang, Y., Jodoin, P.-M., Porikli, F., et al.: CDnet 2014: an expanded change detection benchmark dataset. In: Proceedings of the IEEE Workshop on Change Detection (CDW-2014) at CVPR-2014, pp. 387–394 (2014)

    Google Scholar 

Download references

Acknowledgment

This paper was partially funded by the Natural Science Foundation of Hubei Province (Grant No. 2014CFB585), and the Natural Science Foundation of China (Grant No. 61573002).

Author information

Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan, 430068, China

    HongGang Xie

  2. School of Electronic Information, Wuhan University, Wuhan, 430072, China

    JinSheng Xiao & JunFeng Lei

Authors
  1. HongGang Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. JinSheng Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. JunFeng Lei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to HongGang Xie .

Editor information

Editors and Affiliations

  1. National Institute of Informatics, Tokyo, Japan

    Shin'ichi Satoh

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Xie, H., Xiao, J., Lei, J. (2018). Context-Awareness Based Adaptive Gaussian Mixture Background Modeling. In: Satoh, S. (eds) Image and Video Technology. PSIVT 2017. Lecture Notes in Computer Science(), vol 10799. Springer, Cham. https://doi.org/10.1007/978-3-319-92753-4_32

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-92753-4_32

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92752-7

  • Online ISBN: 978-3-319-92753-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation