Skip to main content
SpringerLink
Log in

A Framework of Moving Object Segmentation in Maritime Surveillance Inside a Dynamic Background

  • Chapter
  • First Online:
Transactions on Computational Science XXV

Part of the book series: Lecture Notes in Computer Science ((TCOMPUTATSCIE,volume 9030))

Abstract

Maritime surveillance represents a challenging scenario for moving object segmentation due to the complexity of the observed scenes. The waves on the water surface, boat wakes, and weather issues contribute to generate a highly dynamic background. Moving object segmentation using change detection under maritime environment is a challenging problem for the maritime surveillance system. To address these issues, a fast and robust moving object segmentation approach is proposed which consist of seven steps applied on given video frames which include wavelet decomposition of frames using complex wavelet transform; use of change detection on detail coefficients (LH, HL, HH); use of background modeling on approximate co-efficient (LL sub-band); cast shadow suppression; strong edge detection; inverse wavelet transformation for reconstruction; and finally using closing morphology operator. For dynamic background modeling in the water surface, we have used background registration, background difference, and background difference mask in the complex wavelet domain. For shadow detection and suppression problem in water surface, we exploit the high frequency sub-band in the complex wavelet domain. A comparative analysis of the proposed method is presented both qualitatively and quantitatively with other standard methods available in the literature for seven datasets. The various performance measures used for quantitative analysis include relative foreground area measure (RFAM), misclassification penalty (MP), relative position based measure (RPM), normalized cross correlation (NCC), Precision (PR), Recall (RE), shadow detection rate (SDR), shadow discrimination rate, execution time and memory consumption. Experimental results indicate that the proposed method is performing better in comparison to other methods in consideration for all the test cases as well as addresses all the issues effectively.

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. El-Sayed, S.K., Ahmed, S.: Moving object detection in spatial domain using background removal techniques - state-of-on computer science. Recent Pat. Comput. Sci. 1, 32–54 (2008)

    Article  Google Scholar 

  2. Cristani, M., Farenzena, M., Bloisi, D., Murino, V.: Background subtraction for automated multisensor surveillance: a comprehensive review. EURASIP J. Adv. Sig. Process. 2010, 1–24 (2010)

    Article  Google Scholar 

  3. Cheung, S-C., Kamath, C.: Robust techniques for background subtraction in urban traffic video. In: Proceedings of the SPIE 5308 Conference of Visual Communications and Image Processing, vol. 5308, pp. 881–892 (2004)

    Google Scholar 

  4. Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Real time foreground background segmentation using codebook model. Real Time Imaging 11, 172–185 (2005)

    Article  Google Scholar 

  5. Kushwaha, A.K.S., Sharma, C.M., Khare, M., Prakash, O., Khare, A.: Adaptive real-time motion segmentation technique based on statistical background model. Imaging Sci. J. 62, 285–302 (2014)

    Article  Google Scholar 

  6. McFarlane, N., Schofield, C.: Segmentation and tracking of piglets in images. Mach. Vis. Appl. 8, 187–193 (1995)

    Article  Google Scholar 

  7. Remagnino, P., Baumberg, A., Grove, T., Hogg, D., Tan, T., Worrall, A., Baker, K.: An integrated traffic and pedestrian model-based vision system. In: Proceedings of the Eighth British Machine Vision Conference, pp. 380–389 (1997)

    Google Scholar 

  8. Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 1999), vol. 2, pp. 246–252 (1999)

    Google Scholar 

  9. Kim, C., Hwang, J.-N.: Fast and automatic video object segmentation and tracking for content-based applications. IEEE Trans. Circ. Syst. Video Technol. 12, 122–129 (2002)

    Article  Google Scholar 

  10. Shih, M.-Y., Chang, Y.-J., Fu, B.-C., Huang, C.-C.: Motion-based back-ground modeling for moving object detection on moving platforms. In: Proceedings of the International Conference on Computer Communications and Networks, pp. 1178–1182 (2007)

    Google Scholar 

  11. Huang, J.C., Hsieh, W.S.: Wavelet based moving object segmentation. Electron. Lett. 39, 1380–1382 (2003)

    Article  Google Scholar 

  12. Huang, J.C., Su, T.S., Wang, L.J., Hsieh, W.S.: Double change detection method for wavelet based moving object segmentation. Electron. Lett. 40, 798–799 (2004)

    Article  Google Scholar 

  13. Baradarani, A., Wu, Q.M.J.: Wavelet based moving object segmentation: from scalar wavelets to dual-tree complex filter banks. In: Herout, A. (ed.) Pattern Recognition Recent Advances. InTech Open Access, Rijeka (2010)

    Google Scholar 

  14. Baradarani, A.: Moving object segmentation using 9/7-10/8 dual tree complex filter bank. In: Proceeding of IEEE 19th International Conference on Pattern Recognition (ICPR), pp. 1–4 (2008)

    Google Scholar 

  15. Khare, M., Srivastava, R.K., Khare, A.: Single change detection-based moving object segmentation by using Daubechies complex wavelet transform. IET Image Proc. 8, 334–344 (2014)

    Article  Google Scholar 

  16. Liu, H., Chen, X., Chen, Y., Xie, C.: Double change detection method for moving-object segmentation based on clustering. In: IEEE ISCAS 2006 Circuits and Systems, pp. 5027–5030 (2006)

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  19. Daubechies, I.: Ten Lectures on Wavelets. SIAM, Philadelphia (1992)

    Book  MATH  Google Scholar 

  20. Khare, M., Srivastava, R.K., Khare, A.: Dual tree complex wavelet transform based shadow detection and removal from moving objects. In: Proceeding of 26th SPIE Electronic Imaging, vol. 9029, pp. 1–7 (2014)

    Google Scholar 

  21. Hsia, C.-H., Guo, J.-M.: Efficient modified direction al lifting-based discrete wavelet transform for moving object detection. J. Sig. Process. 96, 138–152 (2014)

    Article  Google Scholar 

  22. Bloisi, D.D., Pennisi, A., Iocchi, L.: Background modeling in the maritime domain. Mach. Vis. Appl. 25, 1257–1269 (2014)

    Article  Google Scholar 

  23. Aacj, T., Kaup, A., Mester, R.: Statistical model-based change detection in moving video. J. Sig. Process. 31, 165–180 (1993)

    Article  Google Scholar 

  24. Romberg, J.K., Choi, H., Baraniuk, R.G.: Multiscale edge grammars for complex wavelet transforms. In: Proceedings of the IEEE International Conference on Image Processing, pp. 614–617 (2001)

    Google Scholar 

  25. Sridhar, S.: Digital image processing, 3rd edn. Oxford Publication, Oxford (2008)

    Google Scholar 

  26. Bloisi, D., Iocchi, L., Pennisi, A., Previtali, F.: http://www.dis.uniroma1.it/~labrococo/MAR/dataset.htm

  27. Gao-bo, Y., Zhao-yang, Z.: Objective performance evaluation of video segmentation algorithms with Ground-Truth. J. Shanghai University. 8, 70–74 (2004)

    Article  Google Scholar 

  28. Eskicioglu, A.M., Fisher, P.S.: Image quality measures and their performance. IEEE Trans. Commun. 43, 2959–2965 (1995)

    Article  Google Scholar 

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

    Article  Google Scholar 

  30. Wang, Y., Jodoin, P.-M., Porikli, F., Konrad, J., Benezeth, Y., Ishwar, P.: http://changedetection.net/

  31. Subudhi, B.N., Ghosh, S., Ghosh, A.: Change detection for moving object segmentation with robust background construction under wronskian framework. Mach. Vis. Appl. 24, 795–809 (2013)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, 221005, UP, India

    Alok Kumar Singh Kushwaha & Rajeev Srivastava

Authors
  1. Alok Kumar Singh Kushwaha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rajeev Srivastava
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alok Kumar Singh Kushwaha .

Editor information

Editors and Affiliations

  1. University of Calgary, Calgary, Alberta, Canada

    Marina L. Gavrilova

  2. Sardina Systems OÜ, Tallinn, Estonia

    C.J. Kenneth Tan

  3. Bialystok University of Technology, Bialystok, Poland

    Khalid Saeed

  4. University of Calcutta, Kolkata, India

    Nabendu Chaki

  5. NIIT University, Neemrana, India

    Soharab Hossain Shaikh

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Kushwaha, A.K.S., Srivastava, R. (2015). A Framework of Moving Object Segmentation in Maritime Surveillance Inside a Dynamic Background. In: Gavrilova, M., Tan, C., Saeed, K., Chaki, N., Shaikh, S. (eds) Transactions on Computational Science XXV. Lecture Notes in Computer Science(), vol 9030. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47074-9_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-47074-9_3

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-47073-2

  • Online ISBN: 978-3-662-47074-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation