Skip to main content
SpringerLink
Log in

Fast Robust PCA on Background Modeling

  • Conference paper
  • First Online:
Proceedings of 2017 Chinese Intelligent Systems Conference (CISC 2017)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 460))

Included in the following conference series:

  • 1162 Accesses

Abstract

This paper extends the subspace learning method Robust principal component analysis (RPCA) for background modeling to recover the background scene from video sequence with static camera. We propose a novel matrix reformulation and optimization process for RPCA method to solve background modeling problem. The experiments are conducted among our proposed method and other statistical methods including RPCA algorithm and its variants under wallflower datasets and LRSLibrary benchmarks separately. The results of experiments show that our method exceeds the existing RPCA in time complexity in a great manner, while keeps and even improves the modeling performance over other modeling algorithms.

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 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover 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. Chiranjeevi P, Sengupta S. Spatially correlated background subtraction, based on adaptive background maintenance. J Vis Commun Image Represent. 2012;23(23):948–57.

    Article  Google Scholar 

  2. Marghes C, Bouwman T. Background modeling via incremental maximum margin criterion. In: International conference on computer vision. Springer;2010. P. 394–03.

    Google Scholar 

  3. Huang DY, Chen CH, Hu WC, et al. Reliable moving vehicle detection based on the filtering of swinging tree leaves and raindrops. J Vis Commun Image Represent. 2012;23(4):648–64.

    Article  Google Scholar 

  4. Wren CR, Azarbayejani Ali, Darrell Trevor, et al. Real-time tracking of the human body. IEEE Trans Pattern Anal Mach Intell. 1997;19(7):780–5.

    Article  Google Scholar 

  5. Elgammal, A, Harwood D, Davis L. Non-parametric model for background subtraction. Lect Notes Comput Sci.

    Google Scholar 

  6. Stauffer, Chris, Grimson, W.E.L. Adaptive background mixture models for real-time tracking. CVPR. IEEE Comput Soc. 1999;2246.

    Google Scholar 

  7. Barnich O, Van DM. ViBe: a universal background subtraction algorithm for video sequenc-es. IEEE Trans Image Process Publ IEEE Signal Process Soc. 2011;20(6):1709–24.

    Article  Google Scholar 

  8. Oliver N, Rosario B, Pentland A. A Bayesian computer vision system for modeling human interactions. IEEE Trans Pattern Anal Mach Intell. 2000;22(8):831–43.

    Article  Google Scholar 

  9. Pham V Q, Takahashi K, Naemura T. Foreground-background segmentation using iterated distribution matching. In: CVPR, IEEE computer society conference on computer vision and pattern recognition. IEEE computer society conference on computer vision and pat-tern recognition;2011. P. 2113–20.

    Google Scholar 

  10. Brutzer S, Hoferlin B, Heidemann G. Evaluation of background subtraction techniques for video surveillance. In: IEEE conference on computer vision & pattern recognition. IEEE;2011, 1937–44.

    Google Scholar 

  11. Karaman M, Goldmann L, Yu D, et al. Comparison of static background segmentation methods. In: Proceedings of SPIE—The international society for optical engineering, vol. 5960(4);2005. p. 2140–51 2005.

    Google Scholar 

  12. Menze M, Geiger A. Object scene flow for autonomous vehicles. Computer vision and pattern recognition. IEEE;2015.

    Google Scholar 

  13. Appiah K, Hunter AA. Single-Chip FPGA implementation of real-time adaptive background model. In: IEEE international conference on field-programmable technology;2005. P. 95–02.

    Google Scholar 

  14. Kryjak T, Komorkiewicz M, Gorgon M. Real-time background generation and fore-ground object segmentation for high-definition colour video stream in FPGA device. J Real-time Image Process. 2014;9(1):61–77.

    Article  Google Scholar 

  15. Rodriguez-Gomez R, Fernandez-Sanchez EJ, Diaz J, et al. FPGA implementation for real-time background subtraction based on horprasert model. Sensors. 2012;12(1):585–611.

    Article  Google Scholar 

  16. Bouwmans T. Traditional and recent approaches in background modeling for foreground detection: an overview. Comput Sci Rev. 2014;11–12:31–66.

    Google Scholar 

  17. Lee B, Hedley M. Background estimation for video surveillance. IVCNZ. 2002;2002:315–20.

    Google Scholar 

  18. Mcfarlane NJB, Schofield CP. Segmentation and tracking of piglets in images. Mach Vis Appl. 1995;8(3):187–93.

    Article  Google Scholar 

  19. Wang N, Zheng J, Wang Y, et al. Extracting roadway background image: mode-based approach. Trans Res Rec J Trans Res Board. 2006;1944(1).

    Google Scholar 

  20. Wright J, Ganesh A, Rao S, et al. Robust principal component analysis: exact recovery of corrupted low-rank matrices. J ACM. 2009;58(3):11.

    Google Scholar 

  21. Bouwmans T, Baf FE, Vachon B. Statistical background modeling for foreground detection: a survey. Handbook of pattern recognition and computer vision;2009. P. 181–99.

    Google Scholar 

  22. Guyon C, Bouwmans T, Zahzah E H. Robust principal component analysis for background subtraction: systematic evaluation and comparative analysis. Principal component analysis. InTech;2012.

    Google Scholar 

  23. Xiao S, Li W, Xu D, et al. FaLRR: a fast low rank representation solver. In: IEEE conference on computer vision and pattern recognition. IEEE;2015. P. 4612–20.

    Google Scholar 

  24. Sobral A, Bouwmans T, Zahzah EH. LRSLibrary: Low-Rank and sparse tools for background modeling and subtraction in videos. Handbook on “robust low-rank and sparse matrix decomposition: applications in image and video processing”; 2016.

    Google Scholar 

  25. Tang G, Nehorai A. Robust principal component analysis based on low-rank and block-sparse matrix decomposition. Information sciences and systems. IEEE;2011. P. 1–5.

    Google Scholar 

  26. Torre FDL, Black MJ. Robust principal component analysis for computer vision. In: Proceedings of the eighth IEEE international conference on computer vision, 2001. ICCV 2001, vol. 1, IEEE;2001. P. 362–9.

    Google Scholar 

  27. Zhou T, Tao D. GoDec: Randomized low rank & sparse matrix decomposition in noisy case. In: International conference on Machine learning, ICML 2011, Bellevue, Washington, USA, June 28–July. DBLP;2011. P. 33–40.

    Google Scholar 

  28. Rodriguez P, Wohlberg B. Fast principal component pursuit via alternating minimization. In: IEEE international conference on image processing. IEEE;2014. P. 69–73.

    Google Scholar 

  29. Toyama K, Krumm J, Brumitt B, et al. Wallflower: principles and practice of background maintenance. In: IEEE international conference on computer vision, vol. 1, IEEE Xplore;1999. p. 255–61.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, National University of Defense Technology, Changsha, 410000, China

    Huini Fu, Zhihui Gao & HengZhu Liu

Authors
  1. Huini Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhihui Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. HengZhu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huini Fu .

Editor information

Editors and Affiliations

  1. Beihang University , Beijing, China

    Yingmin Jia

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Junping Du

  3. University of Science and Technology Beijing, Beijing, China

    Weicun Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Fu, H., Gao, Z., Liu, H. (2018). Fast Robust PCA on Background Modeling. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2017 Chinese Intelligent Systems Conference. CISC 2017. Lecture Notes in Electrical Engineering, vol 460. Springer, Singapore. https://doi.org/10.1007/978-981-10-6499-9_38

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-6499-9_38

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6498-2

  • Online ISBN: 978-981-10-6499-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation