Skip to main content
SpringerLink
Log in

A Fast Active Contour Tracking Method Based on Gaussian Mixture Model

  • Conference paper
  • First Online:
The Proceedings of the Second International Conference on Communications, Signal Processing, and Systems

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 246))

  • 1014 Accesses

Abstract

This paper proposed a Gaussian mixture model based gradient level set method (GMM-GLS) for moving target contour tracking in video sequences to handle automatic initialization and background variation. In contrast with conventional level set models, adaptive GMM background subtraction is applied to get the rough location of moving target as foreground in current frame. And more accurate mask image according to the rough location of foreground with dilatation operation indicates the initialization contour of level set evolution. Then, the gradient level set model can evolve the curve quickly and ensure more accurate convergence to the target contour in tracking procedure. Based on this accurate mask, the GMM-GLS method can greatly reduce the uncertain iteration time in curve convergence and optimize the initialization of GLS eliminating the interferential background. Experimental results on many real-world video sequences validate that our approach greatly improves the performance of object contour tracking.

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. Kass M, Witkin A, Terzopoulos D (1987) Snakes: active contour models. Int J Comput Vis 1:321–331. doi:10.1007/bf00133570

    Article  Google Scholar 

  2. Xu C, Prince J (1998) Snakes, shapes, and gradient vector flow. IEEE Trans Image Process 7:359–369. doi:10.1109/83.661186

    Article  MATH  MathSciNet  Google Scholar 

  3. Caselles V, Catte F, Coll T, Dibos F (1993) A geometric model for active contours in image processing. Numer Math 66:1–31. doi:10.1007/BF01385685

    Article  MATH  MathSciNet  Google Scholar 

  4. Caselles V, Kimmel R, Sapiro G (1997) Geodesic active contours. Int J Comput Vis 22:61–79. doi:10.1023/A:1007979827043

    Article  MATH  Google Scholar 

  5. Osher S, Sethian JA (1988) Fronts propagating with curvature dependent speed: algorithms based on Hamilton-Jacobi formulation. J Comput Phys 79:12–49. doi:10.1016/0021-9991(88)90002-2

    Article  MATH  MathSciNet  Google Scholar 

  6. Weickert J, Romeny BMH, Viergever MA (1998) Efficient and reliable schemes for nonlinear diffusion filtering. IEEE Trans Image Process 7:398–410. doi:10.1109/83.661190

    Article  Google Scholar 

  7. Li C, Xu C, Gui C (2005) Level set evolution without re-initialization: a new variational formulation. Proc IEEE Conf Comput Vis Pattern Recogn 1:430–436. doi:10.1109/CVPR.2005.213

    Google Scholar 

  8. Mumford D, Shah J (1989) Optimal approximation by piecewise smooth functions and associated variational problems: commun. Comm Pure Appl Math 42:577–685. doi:10.1002/cpa.3160420503

    Article  MATH  MathSciNet  Google Scholar 

  9. Chan T, Vese L (2001) Active contours without edges. IEEE Trans Image Process 10:266–277. doi:10.1109/83.902291

    Article  MATH  Google Scholar 

  10. Rathi Y, Vaswani N, Tannenbaum A, Yezzi A (2005) Particle filtering for geometric active contours with application to tracking moving and deforming objects. IEEE Comput Soc Conf Comput Vis Pattern Recogn 2:2–9. doi:10.1109/CVPR.2005.271

    Google Scholar 

  11. Sun X, Yao H, Zhang S (2011) A novel supervised level set method for non-rigid object tracking. IEEE Conf Digital Object Identifier Comput Vis Pattern Recogn 3393–3400. doi: 10.1109/CVPR.2011.5995656

  12. Zivkovic Z (2004) Improved adaptive Gaussian mixture model for background subtraction. Proceedings of the 17th international conference on pattern recognition, vol 2, pp 23–26. doi: 10.1109/ICPR.2004.1333992

    Google Scholar 

  13. The School of Informatics of the University of Edinburgh (2004) CAVIAR Test Case Scenarios. http://groups.inf.ed.ac.uk/vision/CAVIAR/CAVIARDATA1. Accessed 31 Apr 2013

Download references

Author information

Authors and Affiliations

  1. School of Communication and Information Engineering, Shanghai University, Shanghai, China

    Yuchen Wang, Xiaofeng Lu & Mingyao Zhu

  2. Shanghai Key Laboratory of Digital Media Processing and Transmissions, Shanghai Jiao Tong University, Shanghai, China

    Xiaofeng Lu

Authors
  1. Yuchen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaofeng Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mingyao Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaofeng Lu .

Editor information

Editors and Affiliations

  1. College of Physical and Electronic Infor, Tianjin Normal University, Tianjin, People's Republic of China

    Baoju Zhang

  2. College of Physical and Electronic Infor, Tianjin Normal University, Tianjin, People's Republic of China

    Jiasong Mu

  3. College of Physical and Electronic Infor, Tianjin Normal University, Tianjin, People's Republic of China

    Wei Wang

  4. Department of Electrical Engineering, University of Texas at Arlington, Arlington, Texas, USA

    Qilian Liang

  5. School of electronic engineering, University of Electronic Science and Tec, Chengdu, People's Republic of China

    Yiming Pi

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Wang, Y., Lu, X., Zhu, M. (2014). A Fast Active Contour Tracking Method Based on Gaussian Mixture Model. In: Zhang, B., Mu, J., Wang, W., Liang, Q., Pi, Y. (eds) The Proceedings of the Second International Conference on Communications, Signal Processing, and Systems. Lecture Notes in Electrical Engineering, vol 246. Springer, Cham. https://doi.org/10.1007/978-3-319-00536-2_116

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-00536-2_116

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-00535-5

  • Online ISBN: 978-3-319-00536-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation