Enhancement of Mean Shift Tracking Through Joint Histogram of Color and Color Coherence Vector

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 236)


Tracking of an object in a scene, especially through visual appearance is weighing much relevance in the context of recent research trend. In this work, we are extending the one of the approaches through which visual features are erected to reveal the motion of the object in a captured video. One such strategy is a mean shift due to its unfussiness and sturdiness with respect to tracking functionality. Here we made an attempt to judiciously exploit the tracking potentiality of mean shift to provide elite solution for various applications such as object tracking. Subsequently, in view of proposing more robust strategy with large pixel grouping is possible through mean shift. The mean shift approach has utilized the neighborhood minima of a similarity measure through bhattacharyya coefficient (BC) between the kernel density estimate of the target model and candidate. However, similar capability is quite possible through color coherence vectors (CCV). The CCV are derived in addition to color histogram of target model and target candidate. Further, joint histogram of color model and CCV is added. Thus, the resultant histograms are empirically less sensitive to variance of background which is not ensured through traditional mean shift alone. Experimental results proved to be better and seen changes in tracking especially in similar color background. This work explores the contribution and paves the way for different applications to track object in varied dataset.


Object tracking CCV Mean shift Kernel BC 


  1. 1.
    Ajay, M., Sanjeev, S., Venkatesh, M.: A novel color coherence vector based obstacle detection algorithm for textured environments. The 3rd international conference on machine vision (2010)Google Scholar
  2. 2.
    Birchfield, S.: Elliptical head tracking using intensity gradients and color histograms. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 232–237(1998)Google Scholar
  3. 3.
    Brian, V.: Funt, Graham, D., Finlayson.: Color constant color indexing. IEEE Trans. Pattern Anal. Mach. Intell. 17(5), 522–529 (1995)Google Scholar
  4. 4.
    Chan Nguyen, V.: An efficient obstacle detection algorithm using color and texture. World Acad. Sci. Technol. 36, 132–137 (2009)Google Scholar
  5. 5.
    Cheng, Y.: Mean shift, mode seeking, and clustering. IEEE Trans. Pattern Anal. Ma chine Intell. 17, 790–799 (1995)Google Scholar
  6. 6.
    Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: Proceedings IEEE conference computer vision and pattern recognition IIs 142–149 (2000)Google Scholar
  7. 7.
    Comaniciu, D., Ramesh, V., Meer, P.: PAMI. Kernel based object tracking 25(5), 564–575 (2003)Google Scholar
  8. 8.
    Greg, P., Ramin, Z., Justin, M.: Comparing color images using color cohe rence vector. In: Proceedings 4th ACM international conference on multimedia (1997)Google Scholar
  9. 9.
    Han, B., Davis, L.: Object tracking by adaptive feature extraction. In: Proceedings of the IEEE conference on image processing, 3, 1501–1504 (2004)Google Scholar
  10. 10.
    Ning, J., Zhang, L., Chengke W.: Robust object tracking using joint Co- lor\_Texture histogram. Int. J. Pattern Recogn. Artif. Intell. 23(7), 1245–1263 (2009)Google Scholar
  11. 11.
    Li, X., Wu, F.: Convergence of a mean shift Algorithm. J. Softw. 16(3), 365–374 (2005)CrossRefMATHMathSciNetGoogle Scholar
  12. 12.
    Ning, J., Zhang, L., Zhang, D.: Robust mean shift tracking with corrected background- weighted histogram. IET-CV, (2010)Google Scholar
  13. 13.
    Swain, M., Ballard, D.: Color indexing. Int. J. Comput. Vision 7(1), 11–32 (1991)CrossRefGoogle Scholar
  14. 14.
    Wang, J., Yagi, Y.: Integrating shape and color features for adaptive real-time object tracking. In: Proceedings international conference on robotics and biomimetics, Kunming. 1–6 (2006)Google Scholar
  15. 15.
    Yilmaz, A., Javed, O., Shah, M.: Object tracking. A survey. ACM Comput. Surv. 38(4), 13, (2006). doi: 10.1145/1177352.1177355 Google Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.JSS Research FoundationJSS Technical Institutions CampusMysoreIndia
  2. 2.Department of CS and EGovernment Engineering College ChamarajanagarChamarajanagarIndia

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