Skip to main content
SpringerLink
Log in

A novel Rotational Symmetry Dynamic Texture (RSDT) based sub space construction and SCD (Similar-Congruent-Dissimilar) based scoring model for background subtraction in real time videos

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

Abstract

Background Subtraction (BS) plays an important role in video surveillance system because it provides a focus of attention for moving object detection. But there are still challenging scenarios such as dynamic backgrounds and illumination variation. To achieve greater foreground detection quality under these circumstances, a novel approach called “Rotational Symmetry Dynamic Texture” for Background Subtraction is proposed. The concept of key frame is used to avoid processing overhead. A spatio temporal patch is used to describe motion and appearance of a video sequence. From that patch the proposed Rotational Symmetry Dynamic Texture (RSDT) method will encode the relationship between the referenced pixel and its neighbours, based on the directions that are calculated using the concept of rotational symmetry and line symmetry. A novel “SCD (Similar-Congruent-Dissimilar) based scoring” model is used to estimate appearance consistency and temporal coherence of the video. The two layer approach is used to solve sudden illumination changes and to improve processing speed by adopting mean values of blocks. It is suitable for both indoor and outdoor environments because the subspace construction is purely based on directional codes with rotational symmetry and line symmetry. Metric F-Score is used to measure the performance of the proposed method. Finally this paper provides an effective and robust background subtraction scheme. From performance evaluation, it is observed that Rotational Symmetry Dynamic Texture with SCD based scoring model guarantees accurate background subtraction than the existing literature.

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
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Artac M, Jogan M, Leonardis A (2002) Incremental PCA for on-line visual learning and recognition. International Conference on Pattern Recognition (ICPR’02) 3:30781–30784

    Google Scholar 

  2. Barnich O, Van Droogenbroeck M (2011) Vibe: A universal background subtraction algorithm for video sequences. IEEE Trans Image Process 20(6):1709–1724

    Article  MathSciNet  Google Scholar 

  3. Benezeth Y, Jodoin PM, Saligrama V, Rosenberger C (2009) Abnormal events detection based on spatio-temporal co-occurences. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 2458–2465

  4. Black, MJ and Jepson AD (1998) Eigen tracking: Robust matching and tracking of articulated objects using a view-based representation, Int J Comput Vis, vol. 26, pp. 63–84.

  5. BMC: http://bmc.iut-auvergne.com/

  6. Bouwmans T (2011) Recent Advanced Statistical Background Modeling for Foreground Detection: A Systematic Survey. Recent Patents on Computer Science 4(3):147–176

    Google Scholar 

  7. Bouwmans T, Zahzah E (2014) Robust PCA via principal component pursuit: a review for a comparative evaluation in video surveillance. Comput Vis Image Underst 122:22–34

    Article  Google Scholar 

  8. Brutzer S, Hoferlin B Heidemann G (2011) Evaluation of background subtraction techniques for video surveillance. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1937–1944

  9. Candès E, Li X, Ma Y, Wright J (2011) Robust principal component analysis? Journal of the ACM (JACM) 58(3):1–37

    Article  MathSciNet  MATH  Google Scholar 

  10. Cheung SC, Kamath C (2004) Robust techniques for background subtraction in urban traffic video. Video Communications and Image Processing, SPIE Electronic Imaging 5308(1):881–892

    Google Scholar 

  11. Di Stefano L, Tombari F, Mattoccia S (2007) Robust and accurate change detection under sudden illumination variations. ACCV Workshop on Multi-dimensional and Multi-view Image Process, pp 179–193

  12. Ding X, He L, Carin L (2011) Bayesian robust principal component analysis. IEEE Trans Image Process 20(12):3419–3430

    Article  MathSciNet  Google Scholar 

  13. Doretto G, Cremers D, Favaro P, Soatto S (2003) Dynamic texture segmentation. IEEE International Conference on Computer Vision, pp 1236–1242

  14. Elgammal A, Harwood D, Davis L (2000) Non-parametric Model for Background Subtraction. European Conference on Computer Vision 2:751–767

    Google Scholar 

  15. Farcas D, Marghes C, Bouwmans T (2012) Background subtractionvia incremental maximum margin criterion: A discriminative subspace approach. Mach Vis Appl 23(6):1083–1101

    Article  Google Scholar 

  16. Ferone A, Maddalena L (2014) Neural Background Subtraction for Pan-Tilt-Zoom Cameras. IEEE Trans Syst Man Cybern Syst 44(5):571–579

    Article  Google Scholar 

  17. Guo J, Hsai C, Liu Y, Shih M, Chang C, Wu J (2013) Fast background subtraction based on a multilayer codebook model for moving object detection. IEEE Trans Circuit Syst Video Technol 23(10):1809–1821

    Article  Google Scholar 

  18. Haque M, Murshed M (2013) Perception-inspired background subtraction. IEEE Trans Circuits Syst Video Technol 23(12):2127–2139

    Article  Google Scholar 

  19. Herrmann, DL, Sally PJ, Number, Shape, & Symmetry. https://books.google.co.in/books?isbn=1466554649, pp 257–280

  20. Hyndman RJ, Athanasopoulos G (2015) 8.9 Seasonal ARIMA models

  21. Kamijo S, Ikeuchi K, Sakauchi M (2001) Segmentations of spatio-temporal images by spatio-temporal Markov random field model. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR ) Workshop, pp 298–313

  22. Kim K, Chalidbhongs T, Harwood, Davis L (2004) Background modelling and subtraction by codebook construction. IEEE International. Conference on Image Processing, pp 3061–3064

  23. Kim K, Chalidbhongs T, Harwood, Davis L (2005) Real time foreground background segmentation using codebook model. Real-Time Imaging 11(3):172–185

    Article  Google Scholar 

  24. Levy A, Lindenbaum M (2000) Sequential karhunen-loeve basis extraction and its application to images. IEEE Trans Image Process 9(8):1371–1374

    Article  MATH  Google Scholar 

  25. Li Y (2004) On incremental and robust subspace learning. Pattern Recogn 37(7):1509–1518

    Article  MATH  Google Scholar 

  26. Li L, Leung MKH (2002) Integrating intensity and texture differences for robustchange detection. IEEE Trans Pattern Anal Mach Intell 11(2):105–112

    Google Scholar 

  27. Li L, Huang W, Gu I, Tian Q (2003) Foreground object detection from videos containing complex background. ACM International Conference on Multimedia, pp 2–10

  28. Li L, Wang P, Hu Q, Cai S (2015) A sparse outliers iterative removal algorithm to model the background in the video sequences, Pattern Recognition, preprint

  29. Lin L, Wu T, Porway J, Xu Z (2009) A stochastic graph grammar for compositional object representation and recognition. Pattern Recogn 42(7):1297–1307

    Article  MATH  Google Scholar 

  30. Lin L, Xu Y, Liang X, Lai J (2014) Complex background subtraction by pursuing dynamic spatio – temporal models. IEEE Trans Image Process 23(7):3191–3201

    Article  MathSciNet  Google Scholar 

  31. Liu S, Fu C (1998) Statistical change detection with moments under time varying illumination. IEEE Trans Image Process 7(9):1258–1268

    Article  Google Scholar 

  32. Liu G, Zhao J (2009) Key Frame Extraction from MPEG Video Stream. Proceedings of the Second Symposium International Computer Science and Computational Technology(ISCSCT ‘09)

  33. Liu X, Zhao G, Yao J, Qi C (2015) Background subtraction based on low-rank and structured sparse decomposition. IEEE Trans Image Process 24(8):2502–2514

  34. Manzanera A (2007) Σ-∆ Background Subtraction and the Zipf Law. Pattern Recognition, Image Analysis and Applications 4756:42–51

    Article  Google Scholar 

  35. Mittal A, Paragios N (2004) Motion-based background subtraction using adaptive kernel density estimation. IEEE Proceedings on Computer Vision and Pattern Recognition, II-302-II-309

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

    Article  Google Scholar 

  37. Panda D, Sukadev M (2016) Detection of moving objects using fuzzy color difference histogram based background subtraction. IEEE Signal Process Lett 23(1):45–49

    Article  Google Scholar 

  38. Pawlak M, Holzmann H (2011) Statistical assessment of image symmetries. IEEE Statistical Signal Processing Workshop (SSP), pp 805–808

  39. PETS:http://perception.i2r.a-star.edu.sg

  40. Piccardi M (2004) Background subtraction techniques: a review. IEEE International Conference on Systems, Man, Cybernetics, pp 3099–3104

  41. Pilet J, Strecha C, Fua P (2008)Making background subtraction robust to suddenillumination changes. European Conference on Computer Vision (ECCV), pp 567–580

  42. Power P, Schoonees J (2002) Understand background mixture models for foreground segmentation. Image and Vision Computing, pp 267–271

  43. Prez M (2014) Time Series Analysis With Matlab: Arima and Arimax Models, createspace independent publishing platform

  44. Seki M, Wada T, Fujiwara H, Sumi K (2003) Background subtraction based on cooccurrence of image variations. IEEE International Conference on Computer Vision and Pattern Recognition, pp 65–72

  45. Sheikh Y, Shah M (2005) Bayesian modeling of dynamic scenes for object detection. IEEE Trans Pattern Anal Mach Intell 27(11):1778–1792

    Article  Google Scholar 

  46. Skifstad K, Jain R (1989) Illumination independent change detection for real world image sequences. Computer Vision, Graphics, and Image Processing 46(3):387–399

    Article  Google Scholar 

  47. Skocaj D, Leonardis A (2003) Weighted and robust incremental method for subspace learning. IEEE International Conference on Computer Vision, pp 1494–1501

  48. Soatto S, Doretto G, Wu Y (2003) Dynamic textures. Int J Comput Vis 52(2):91–109

    MATH  Google Scholar 

  49. Stauffer C and Grimson W (1999) Adaptive background mixture models for real-time tracking. IEEE Conference on Computer Vision and Pattern Recognition, pp 2246–2252

  50. Tombari F, Di Stefano L, Mattoccia S (2007) A robust measure for visual correspondence. International Conference on Image Analysis and Processing, pp 376381

  51. Varcheie P, Sills-Lavoie M, Bilodeau GA (2012) A multiscale region- based motion detection and background subtraction algorithm. Sensors 10:1041–1061

    Article  Google Scholar 

  52. Vosters PJ, Shan C, Gritti T (2010) Background subtraction under sudden illumination changes. IEEE Conference on Advanced Video and Signal-Based Surveillance, pp 384–391

  53. Wang L, Wang L, Wen M, Zhuo Q, Wang W (2007) Background subtraction using incremental subspace learning. IEEE International Conference on Image processing (ICIP), pp V-45–V-48

  54. Weyl H (1952) Symmetry, Princeton University Press

  55. Wiltshir, RotationalSymmetry, https://books.google.co.in/books?isbn=0954749979

  56. Wren C, Azarbayejani A, Darrel T, Pentland AP (1997) Real time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence, pp 780–785

  57. Xin B , Tian Y, Wang Y, Gao W (2015) Background Subtraction via Generalized Fused Lasso Foreground Modeling. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 4676–4684

  58. Xue G, Sun J, Song L (2010) Dynamic background subtraction based on spatial extended center-symmetric local binary pattern. International Conference in Multimedia and Exposition, ICME 2010:1050–1055

    Google Scholar 

  59. Zhao X, He W, Luo S, Zhang L (2007) MRF-based adaptive approach for foreground segmentation under sudden illumination change, International Conference on Information and Communication Systems, pp 1–4

  60. Zivkovic Z, van der Heijden F (2006) Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recogn Lett 27(7):773–780

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Anna University Regional Campus - Tirunelveli, Tirunelveli, India

    Jeyabharathi D &  Dejey D

Authors
  1. Jeyabharathi D
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dejey D
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jeyabharathi D.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jeyabharathi D, Dejey D A novel Rotational Symmetry Dynamic Texture (RSDT) based sub space construction and SCD (Similar-Congruent-Dissimilar) based scoring model for background subtraction in real time videos. Multimed Tools Appl 75, 17617–17645 (2016). https://doi.org/10.1007/s11042-016-3772-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-3772-9

Keywords

Search

Navigation