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

  • Dejun Wang
  • Yongtao Shi
  • Weiping SunEmail author
  • Shengsheng Yu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8866)


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.


Target tracking Weighted histogram Foreground feature saliency 


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  1. 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. 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)CrossRefGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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. 5.
    Isard, M., Blake, A.: CONDENSATION—Conditional Density Propagation for Visual Tracking. Int. J. Comput. Vis. 29(1), 5–28 (1998)CrossRefGoogle Scholar
  6. 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)CrossRefGoogle Scholar
  7. 7.
    Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–577 (2003)CrossRefGoogle Scholar
  8. 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)CrossRefGoogle Scholar
  9. 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)CrossRefMathSciNetGoogle Scholar
  10. 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. 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

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Dejun Wang
    • 1
  • Yongtao Shi
    • 2
  • Weiping Sun
    • 1
    Email author
  • Shengsheng Yu
    • 1
  1. 1.School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina
  2. 2.Department of Computer ScienceThree Gorge UniversityHubeiChina

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