Skip to main content
SpringerLink
Log in

Improved background modeling of video sequences using spatio-temporal extension of fuzzy local binary pattern

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Background subtraction is a method of motion detection in video sequences captured by static camera based on construction of a background model and its progressive comparison with each frame of the video. Sometimes the changes in the background objects are not permanent and appear at a rate faster than that of the background update, and this leads to emergence of dynamic textures in the background. As a result, high-quality background modeling plays a major role in motion detection performance, especially in videos with dynamic backgrounds and adverse environmental conditions such as noise. Although Local Binary Pattern (LBP) is a successful methodology for background subtraction, but it cannot properly extract textures from uniform areas of the foreground. In recent years, Fuzzy Local Binary Pattern (FLBP) has been developed to improve the performance of LBP operator in texture extraction from images with additive noise. The use of FLBP texture descriptor for background subtraction has led to improved robustness against noise and low sensitivity of the background model to slight changes in the texture gray scale values, and therefore better texture extraction from uniform areas, even in the presence of dynamic backgrounds and adverse environmental conditions. But despite this improvement, this operator is still sensitive to time-variations of pixels in dynamic backgrounds. To avoid this issue and incorporate correlation of pixel values over successive frames, this study proposes the use of spatio-temporal extension of FLBP with symmetry about central pixel in combination with the Local Color Histogram (LCH) in the Improved Hue Luminance and Saturation (IHLS) color space for describing the pixel color features. The results of tests conducted with standard databases show that for dynamic backgrounds, the use of proposed Spatio-Temporal Fuzzy Center Symmetric Local Binary Pattern (STFCS-LBP) operator with the spatio-temporal neighborhood texture patterns and the local color histogram yields better results than the existing methods.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Ayub M (2009) Choquet and Sugeno integrals. Thesis for the degree Master of Science in Mathematical Modeling and Simulation

  2. Barnich O, Droogenbroeck MV (2011) ViBe : a universal background subtraction algorithm for video sequences. IEEE Trans Image Process 20(6):1709–1724

    Article  MathSciNet  MATH  Google Scholar 

  3. Bianco S, Ciocca G, Schettini R (2017) Combination of video change detection algorithms by genetic programming. IEEE Trans Evol Comput 21(6):914–928

    Article  Google Scholar 

  4. Bilodeau G-A, Jodoin J-P, Saunier N (2013) Change detection in feature space using local binary similarity patterns. Proc Int Conf Comput Robot Vision (CRV): 106–112

  5. Bouwmans T, Porikli F, Horferlin B, Vacavant A (2014) Handbook on background modeling and foreground detection for video surveillance. CRC press, Taylor and Francis group

  6. Brutzer S, Hӧferlin B, Heidemann G (2011) Evaluation of background subtraction techniques for video surveillance. Proc IEEE Conf Comput Vision Pattern Recogn (CVPR): 1937–1944

  7. Candès EJ, Li X, Ma Y, Wright J (2011) Robust principal component analysis? J ACM 58(3):11

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  9. Chua T W, Leman K, Wang Y (2012) Fuzzy rule-based system for dynamic texture and color based background subtraction. 2012 IEEE Int Conf Fuzzy Syst (FUZZ-IEEE), 1559–1565

  10. El Baf F, Bouwmans T, Vachon B (2008) Fuzzy integral for moving object detection. Proc IEEE Int Conf Fuzzy Syst: 1729–1736

  11. Elgammal A, Harwood D, Davis L (2000) Non-parametric model for background subtraction. Proc 5th Eur Conf Comput Vision 1843

  12. Gemignani G, Rozza A (2016) A robust approach for the background subtraction based on multi-layered self-organizing maps. IEEE Trans Image Process 25(11):5239–5251

    Article  MathSciNet  MATH  Google Scholar 

  13. Grabisch M, Nguyen HT, Walker EA (1995) Fundamentals of university calculi with applications to fuzzy inference. Kluwer Academic

  14. Grabisch M, Murofushi T, Sugeno M (2000) Fuzzy Measure and Integrals. Theory and Applications (editedvolume). Studies in Fuzziness, Physica Verlag

  15. Guo L, Xu D, Qiang Z (2016) Background subtraction using local SVD binary pattern. Proc IEEE Int Conf Comput Vision Pattern Recogn Workshops: 1159–1167

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

    Article  Google Scholar 

  17. Hanbury A (2003) A 3D-polar coordinate colour representation well adapted to image analysis. Proc Scandinav Conf Image Anal (SCIA) 2749

  18. Hasan S, Samson Cheung S-C (2017) Universal multimode background subtraction. IEEE Trans Image Process 26(7):3249–3260

    Article  MathSciNet  MATH  Google Scholar 

  19. Heikkila M, Pietikäinen M (2006) A texture-based method for modeling the background and detecting moving objects. IEEE Trans Pattern Anal Mach Intell 28(4):657–662

    Article  Google Scholar 

  20. Heikkila M, Pietikainen M, Schmid C (2009) Description of interest regions with local binary patterns. Pattern Recogn 42(3):425–436

    Article  MATH  Google Scholar 

  21. Hu Q, Li Sh, He K, Lin H (2010) A robust fusion method for vehicle detection in road traffic surveillance. Proc 6th Int Conf Intell Comput (ICIC) 6216

  22. Iakovidis DK, Keramidas EG, Maroulis D (2008) Fuzzy local binary patterns for ultrasound texture characterization. 2008 Springer Int Conf Image Anal Recogn 5112:750–759

    Google Scholar 

  23. Jiang S, Lu X (2017) WeSamBE : a weight-sample-based method for background subtraction. IEEE Trans Circ Syst Video Technol

  24. Kim K, Chalidabhongse T, Harwood D (2004) Background modeling and subtraction by codebook construction. 2004 IEEE Int Conf Image Process (ICIP): 3061–3064

  25. Lai A, Yung N (1998) A fast and accurate scoreboard algorithm for estimating stationary backgrounds in an image sequence. IEEE Int Sym Circ Syst (ISCAS): 241–244

  26. Lee D–S (2005) Effective Gaussian mixture learning for video background subtraction. IEEE Trans Pattern Anal Mach Intell 27(5):827–832

    Article  Google Scholar 

  27. Liao WH (2012) Incorporating fuzziness in extended local ternary patterns. IEEE Int Sym Multimed (ISM)

  28. Liao S, Zhao G, Kellokumpu V, Pietikäinen M, Li S Z (2010) Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes. Proc IEEE Conf Comput Vision Pattern Recogn (CVPR): 1301–1306

  29. Lipton A J, Fujiyoshi H, Patil RS (1998) Moving target classification and tracking from Real-time video. 1998 Proc IEEE Workshop Appl Comput Vision: 8–44

  30. Maddalena L, Petrosino A (2008) A self-organizing approach to background subtraction for visual surveillance applications. IEEE Trans Image Process 17(7):1168–1177

    Article  MathSciNet  Google Scholar 

  31. Martins I, Carvalho P, Corte-Real L, Alba-Castro JL (2017) BMOG : boosted Gaussian mixture model with controlled complexity. 2017 IbPRIA Pattern Recogn Image Anal: 50–57

  32. Mason M, Duric Z (2001) Using histograms to detect and track objects in color video. Proc IEEE 30th Appl Imag Pattern Recogn Workshop (AIPR)

  33. Mateos G, Giannakis GB (2010) Sparsity control for robust principal component analysis. 2010 Conf Rec Forty Fourth Asilomar Conf Sign Syst Comput (ASILOMAR): 1925–1929

  34. Ojala T, Pietikäinen M, Harwood D (1996) A camparative study of texture measure with classification based on feature distributions. Pattern Recogn 29(1):51–59

    Article  Google Scholar 

  35. Ojala T, Pietikäinen M, Mäenpää T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Analy Mach Intell 24(7):971–987

    Article  MATH  Google Scholar 

  36. Oliver NM, Rosario B, Pentland AP (2000) A Bayesian computer vision system for modeling human interactions. IEEE Trans Pattern Anal Mach Intell (TPAMI) 22(8):831–843

    Article  Google Scholar 

  37. Pietikäinen M, Ahonen T (2008) Soft histograms for local binary patterns. Proc Finnish Sign Process Symp (FINSIG)

  38. Pietikäinen M, Hadid A, Zhao G, Ahnen T (2011) Local binary patterns for still images, Chapter 2. Springer, Computer Vision Using Local Binary Patterns, XVI, p. 212, Hardcover

  39. Randen T, Husøy JH (1999) Filtering for texture classification: a comparative study. IEEE Trans Pattern Anal Mach Intell 21(4):291–310

    Article  Google Scholar 

  40. Ren J, Jiang X, Yuan J (2013) Noise-resistant local binary pattern with an embedded error-correction mechanism. IEEE Trans Image Process 22(10):4049–4060

    Article  MathSciNet  MATH  Google Scholar 

  41. Sigari M, Mozayani N, Pourreza H (2008) Fuzzy running average and fuzzy background subtraction: concepts and application. Int J Comput Sci Netw Sec (IJCSNS) 8 (2) : 138–143

  42. Sobral A, Vacavant A (2014) A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos. Comput Vis Image Underst 122:4–21

    Article  Google Scholar 

  43. Stauffer C, Grimson W (1999) Adaptive background mixture models for real-time tracking. Proc IEEE Conf Comput Vision Pattern Recogn (CVPR)

  44. St-Charles P-L, Bilodeau G-A, Bergevin R (2014) SuBSENSE : a universal change detection method with local adaptive sensitivity. IEEE Trans Image Process 24(1):359–373

    Article  MathSciNet  MATH  Google Scholar 

  45. St-Charles PL, Bilodeau GA, Bergevin R (2015) SuBSENSE : a universal change detection method with local adaptive sensitivity. IEEE Trans Image Process 24(1):359–373

    Article  MathSciNet  MATH  Google Scholar 

  46. Stefano L, Tombari F, Mattoccia S, Lisi E (2007) Robust and accurate change detection under sudden illumination variations. 2007 ACCV Workshop Multi-Dimensional Multi-view Image Process

  47. Szummer M, Picard RW (1996) Temporal texture modeling. 1996 IEEE Int Conf Image Process: 823–826

  48. Tan X, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Processing 19(6):1635–1650

    Article  MathSciNet  MATH  Google Scholar 

  49. Tian Y, Wang Y, Hu Z, Huang T (2013) Selective Eigenbackground for background modeling and subtraction in crowded scenes. IEEE Trans Circ Syst Video Technol 23(11):1849–1864

    Article  Google Scholar 

  50. Tombari F, Stefano L, Mattoccia S (2007) A robust measure for visual correspondence. 2007 Proc IEEE 14th Int Conf Image Anal Process (ICIAP): 376–381

  51. Wang Z, Klir GJ (1992) Fuzzy measure theory. Plenum

  52. Wang K, Liu Y, Guo C, Wang F-Y (2016) A multi-view learning approach to foreground detection for traffic surveillance applications. IEEE Trans Vehic Technol 65(6):4144–4158

    Article  Google Scholar 

  53. Wang Y, Luo ZM, Jodoin PM (2016) Interactive deep learning method for segmenting moving objects. Pattern Recogn Lett 96:66–75

    Article  Google Scholar 

  54. Wang K, Gou C, Wang F-Y (2018) M4CD: A Robust Change Detection Method for Intelligent Visual Surveillance arXiv:1802.04979

  55. Zeng D, Zhu M, Zhou T, Xu F, Yang H (2017) Robust background subtraction via the local similarity statistical descriptor. Appl Sci 7(10):989

    Article  Google Scholar 

  56. Zeng D, Zhu M, Zhou T, Xu F (2018) An extended scale invariant local binary pattern for background subtraction. IET Image Process

  57. Zhang H, xu D (2006) Fusing color and texture features for background model. Proc 3th Int Conf Fuzzy Syst Knowl Discov (ICFSKD) 4223(7):887–893

    Article  Google Scholar 

  58. Zhang B, Gao Y, Zhao S, Zhong B (2011) Kernel similarity modeling of texture pattern flow for motion detection in complex background. IEEE Trans Circ Syst Video Technol 21(1):29–38

    Article  Google Scholar 

  59. Zhao X, Satoh Y, Takauji H, Kaneko S, Iwata K, Ozaki R (2011) Object detection based on a robust and accurate statistical multi-point-pair model. Pattern Recogn 44:1296–1311

    Article  Google Scholar 

  60. Zheng W, Wang K, Wang F-Y (2017) A novel background subtraction algorithm based on parallel vision and Bayesian GANs. Adv Neural Inform Process Syst 30 (NIPS)

  61. Zivkovic Z (2004) Improved adaptive Gaussian mixture model for background subtraction. In: proceedings of the IEEE 17th. Int Conf Pattern Recog (ICPR) 2:28–31

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, University of Guilan, Guilan, Iran

    Akram Norouzi Sefidmazgi & Manoochehr Nahvi

Authors
  1. Akram Norouzi Sefidmazgi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manoochehr Nahvi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manoochehr Nahvi.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Norouzi Sefidmazgi, A., Nahvi, M. Improved background modeling of video sequences using spatio-temporal extension of fuzzy local binary pattern. Multimed Tools Appl 78, 17287–17316 (2019). https://doi.org/10.1007/s11042-018-6972-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6972-7

Keywords

Search

Navigation