Skip to main content

Object Tracking with a Novel Method Based on FS-CBWH within Mean-Shift Framework

  • Conference paper
  • First Online:
Advances in Neural Networks – ISNN 2014 (ISNN 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8866))

Included in the following conference series:

Abstract

Effective appearance models are one critical factor for robust object tracking. In this paper, we introduce foreground feature salience concept into the background modelling, and put forward a novel foreground salience-based corrected background weighted-histogram (FS-CBWH) scheme for object representation and tracking, which exploits salient features of both foreground and background. We think that background and foreground salient features are both crucial for object representation and tracking. Experimental results show that the proposed FS-CBWH scheme can improve the robustness and performance of mean-shift tracker significantly especially in heavy occlusions and large background variation scenes.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Bradski, G.R.: Real time face and object tracking as a component of a perceptual user interface. In: Fourth IEEE Workshop on Applications of Computer Vision, pp. 214–219. IEEE Press, New York (1998)

    Google Scholar 

  2. Papanikolopoulos, N.P., Khosla, P.K., Kanade, T.: Visual tracking of a moving target by a camera mounted on a robot: a combination of control and vision. IEEE Trans. Robotics and Automation 9(1), 14–35 (1993)

    Article  Google Scholar 

  3. Stauffer, C., Grimson, W.E.L.: Learning Patterns of Activity Using Real-Time Tracking. IEEE Trans. Pattern Anal. and Mach. Intell. 22(8), 747–757 (2000)

    Article  Google Scholar 

  4. Devi, M.S., Bajaj, P.R.: Active Facial Tracking. In: 3rd International Conference on Emerging Trends in Engin. and Tech., pp. 91–95. IEEE Press, New York (2010)

    Google Scholar 

  5. Isard, M., Blake, A.: CONDENSATION—Conditional Density Propagation for Visual Tracking. Int. J. Comput. Vis. 29(1), 5–28 (1998)

    Article  Google Scholar 

  6. Pérez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-Based Probabilistic Tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part I. LNCS, vol. 2350, pp. 661–675. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  7. Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–577 (2003)

    Article  Google Scholar 

  8. Vojir, T., Noskova, J., Matas, J.: Robust Scale-Adaptive Mean-Shift for Tracking. In: Kämäräinen, J.-K., Koskela, M. (eds.) SCIA 2013. LNCS, vol. 7944, pp. 652–663. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  9. Ning, J.F., Zhang, L., Zhang, D., Wu, C.: Robust mean-shift tracking with corrected background-weighted histogram. IET Comput. Vision 6(1), 62–69 (2010)

    Article  MathSciNet  Google Scholar 

  10. Wang, L.F., Pan, C.H., Xiang, S.M.: Mean-shift tracking algorithm with weight fusion strategy. In: 18th Inter. Conf. on Image Proc., pp. 473–476. IEEE Press, New York (2011)

    Google Scholar 

  11. Adam, A., Rivlin, E., Shimshoni, I.: Robust fragments-based tracking using the integral histogram. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 798–805. IEEE Press, New York (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Weiping Sun .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Wang, D., Shi, Y., Sun, W., Yu, S. (2014). Object Tracking with a Novel Method Based on FS-CBWH within Mean-Shift Framework. In: Zeng, Z., Li, Y., King, I. (eds) Advances in Neural Networks – ISNN 2014. ISNN 2014. Lecture Notes in Computer Science(), vol 8866. Springer, Cham. https://doi.org/10.1007/978-3-319-12436-0_56

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-12436-0_56

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12435-3

  • Online ISBN: 978-3-319-12436-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics