Skip to main content
SpringerLink
Log in

A framework for background modelling and shadow suppression for moving object detection in complex wavelet domain

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

Abstract

This paper describes a simple, robust and efficient framework for background subtraction and cast shadow suppression in complex wavelet domain. A background subtraction approach exploiting noise resilience capability of wavelet domain combined with local spatial coherence and median filter in the training stage is proposed. A novel shadow suppression scheme based on directional coefficients of Daubechies complex wavelet transform is introduced. The effectiveness of the proposed approach is demonstrated via qualitative and quantitative evaluation measures on both indoor and outdoor video sequences. The experimental results show that the proposed approach outperforms state-of-the-art 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
Fig. 14

Similar content being viewed by others

References

  1. ATON, Autonomous Agents for On-Scene Networked Incident Management, 2000, [Online]. Available: http://cvrr.ucsd.edu/aton/testbed/

  2. Avery RP, Zhang G, Wang Y, Nihan N (2007) An investigation into shadow removal from traffic images transportation research record, J Transport Res Board

  3. Cheng FH, Chen YL (2006) Real time multiple objects tracking and identification based on discrete wavelet transform. Pattern Recogn 39(6):1126–1139

    Article  MATH  Google Scholar 

  4. Chien SY, Ma SY, Chen LG (2002) Efficient moving object segmentation algorithm using background registration technique. IEEE Trans Circ Syst Video Tech 12:577–585

    Article  Google Scholar 

  5. Clonda D, Lina JM, Goulard B (2004) Complex Daubechies wavelets: properties and statistical image modelling. Signal Process (Elsevier) 84:1–23

    Article  MATH  Google Scholar 

  6. Cucchiara R, Piccardi M, Prati A (2003) Detecting moving objects, ghosts, and shadows in video streams. IEEE Trans Pattern Anal Mach Intell 25(10):1337–1342

    Article  Google Scholar 

  7. Elgammal A, Duraiswami R, Harwood D, Davis LS (2002) Background and Foreground Modeling using Non-parametric Kernel Density Estimation for Visual Surveillance, Proceedings of the IEEE, pp 1151–1163

  8. Fang LZ, Qiong WY, Sheng YZ (2008) A method to segment moving vehicle cast shadow based on wavelet transform. Pattern Recognit Lett 29(16):2182–2188

    Article  Google Scholar 

  9. Guan Y-P (2010) Spatio-temporal motion-based foreground segmentation and shadow suppression. IET Comput Vis 4(1):50–60

    Article  Google Scholar 

  10. Heikkila M, Pietikainen 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 

  11. Hu WM, Tan TN, Wang L, Maybank S (2004) A survey on visual surveillance of object motion and behaviors. IEEE Trans Syst Man Cybern 34(3):334–352

    Article  Google Scholar 

  12. Huang J, Hsieh W (2003) Wavelet-based moving object segmentation. IEE Electron Lett 39(19):1380–1382

    Article  Google Scholar 

  13. Huang J, Hsieh W (2004) Double-change-detection method for wavelet-based moving-object segmentation, IEE Electron Lett, vol. 40

  14. Kim H, Sakamoto R, Kitahara I, Toriyama T, Kogure K (2007) Robust silhouette extraction technique using background subtraction with multiple thresholds, Opt Eng 46(9)

  15. Lina J-M (1997) Image processing with complex Daubechies wavelets. J Math Imag Vis 7(3):211–223

    Article  MathSciNet  Google Scholar 

  16. Lu Y, Xin H, Kong J, Li B, Wang Y (2006) Shadow removal based on shadow direction and shadow attributes, Proc. Int. Conf. Computational Intelligence for Modelling, Control and Automation, and Intelligent Agents, Web Technologies and Internet Commerce, pp 37–41

  17. McKenna SJ, Jabri S, Duric Z, Wechsler H (2000) Tracking interacting people, Proc IEEE Int Conf Automat Face Gesture Recogn, pp 348–353

  18. Parks DH, Fels SS (2008) Evaluation of Background Subtraction Algorithms with Post-processing, Proc IEEE Int Conf Adv Video Signal-based Surveill, pp 192–199

  19. PETS, Performance Evaluation of Tracking and Surveillance, 2009, [Online]. Available: http://www.cvg.rdg.ac.uk/PETS2009/a.html

  20. Piccardi M (2004) Background subtraction techniques: a review, Proc IEEE Int Conf Syst Man Cybern, pp 3099–3104

  21. Prati A, Mikic I, Trivedi MM, Cucchiara R (2003) Detecting moving shadows: algorithms and evaluation. IEEE Trans Pattern Anal Mach Intell 25(6):918–923

    Article  Google Scholar 

  22. Romberg JK, Choi H, Baraniuk RG (2001) Multiscale edge grammars for complex wavelet transforms, Proc IEEE Int Conf Image Proc, pp 614–617

  23. Salvador E, Cavallaro A, Ebrahimi T (2004) Cast shadow segmentation using invariant color features. Comput Vis Image Understand 95(3):238–259

    Article  Google Scholar 

  24. Selesnick IW, Baraniuk RG, Kingsbury NG (2005) The Dual-Tree Complex Wavelet Transform, IEEE Signal Process Mag, pp 123–151

  25. Stauder J, Mech R, Ostermann J (1999) Detection of moving cast shadows for object segmentation. IEEE Trans Multimed 1(1):65–76

    Article  Google Scholar 

  26. Stauffer C, Grimson WEL (1999) Adaptive Background Mixture Models for Real-Time Tracking, Proc IEEE Int Conf Comput Vis Pattern Recogn, pp 246–252

  27. VSMFD, Video Scene Modeling and Foreground Detection, 2008 [Online], Available: http://www.kedarpatwardhan.org/Research/VideoSceneModelling.html

  28. Wren C, Azarbayejani A, Darrell T, Pentland AP (1997) Pfinder: real time tracking of the human body. IEEE Trans Pattern Anal Mach Intell 19(7):780–785

    Article  Google Scholar 

  29. Yilmaz A, Javed O, Shah M (2006) Object Tracking: A Survey, ACM J Comput Surv 38:(4), Article 13

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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. GLA University, Mathura, 281406, India

    Anand Singh Jalal

  2. Indian Institute of Information Technology, Allahabad, 211012, India

    Vrijendra Singh

Authors
  1. Anand Singh Jalal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vrijendra Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anand Singh Jalal.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Jalal, A.S., Singh, V. A framework for background modelling and shadow suppression for moving object detection in complex wavelet domain. Multimed Tools Appl 73, 779–801 (2014). https://doi.org/10.1007/s11042-012-1326-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-012-1326-3

Keywords

Search

Navigation