Advertisement

Cluster Computing

, Volume 22, Supplement 3, pp 6079–6090 | Cite as

An object tracking method based on Mean Shift algorithm with HSV color space and texture features

  • Jinhang LiuEmail author
  • Xian Zhong
Article
  • 135 Downloads

Abstract

Mean Shift is a powerful and versatile non-parametric iterative algorithm that can be used for lot of purposes like finding modes, clustering etc. It has been widely used in target tracking field because of some advantages like fewer iteration times and better real-time performance for many years. However, due to only single-color histogram representation of target feature has been used in traditional Mean Shift algorithm, it cannot track very well in some cases, especially under very complicated conditions. There are mainly two problems that can cause traditional Mean Shift algorithm to be unstable. The first problem is when the background color and target color are similar, the tracking performance is significantly insufficient, the second is the partial occlusion problem. In this paper, we have proposed a solution to solve these two issues, which contains three improvements. For the first problem, we transformed original color features in traditional Mean Shift algorithm into HSV color space, At the same time, we will also consider texture features and integrate into algorithm to improve tracking performance. and, we applied four neighborhood searching method to solve partial occlusion problem. We tested our algorithms on a variety of standard datasets and a video data collected from real-world environment. The result of experiments show that our proposed algorithm has higher accuracy than traditional Mean Shift algorithm and the background weighted Mean Shift algorithm in test case of complex conditions. Besides, our proposed algorithm also has a good operating efficiency then traditional one.

Keywords

Target tracking Color feature Texture feature Mean Shift 

References

  1. 1.
    Fukunaga, F., Hostetler, L.D.: The estimation of the gradient of a density function with applications in pattern recognition. IEEE Trans. Inf. Theory 21(1), 32–40 (1975)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Bradski, G.: Computer vision face tracking for use in a perceptual user interface. Intel Technol. J. 2(Q2), 1–15 (1998)Google Scholar
  3. 3.
    Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–577 (2003)CrossRefGoogle Scholar
  4. 4.
    Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 142–149 (2000)Google Scholar
  5. 5.
    Ning, J., Zhang, L., Zhang, D., et al.: Robust mean shift tracking with corrected background-weighted histogram. ET Comput. Vis. 6(1), 62–69 (2012)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefGoogle Scholar
  7. 7.
    Ning, J., Zhang, L., Zhang, D., et al.: Robust object tracking using joint color-texture histogram. Int. J. Pattern Recogn. Artif. Intell. 23(7), 1245–1263 (2009)CrossRefGoogle Scholar
  8. 8.
    Ponomarev, A., Nalamwar, H.S., Babakov, I., et al.: Content-based image retrieval using color, texture and shape features. Key Eng. Mater. 685, 872–876 (2016)CrossRefGoogle Scholar
  9. 9.
    Hidayatullah, P., Zuhdi, M.: Color-texture based object tracking using HSV color space and local binary pattern. Int. J. Electr. Eng. Inform. 7, 161–174 (2015)CrossRefGoogle Scholar
  10. 10.
    Ji, Q.: Auto face detection and tracking algorithm combining color and texture features based on particle filter. J. Inf. Comput. Sci. 11(17), 6327–6336 (2014)CrossRefGoogle Scholar
  11. 11.
    Wang, B., Fan, B.: Adoptive meanshift tracking algorithm based on the feature histogram of color and teature. J. Nanjing Univ. Posts Telecommun. 33(03), 18–25 (2013)Google Scholar
  12. 12.
    Yao, Y., Li, F., Zhou, S.: Target tracking based on color and the texture feature. Comput. Eng. Sci. 36(8), 1581–1587 (2014)Google Scholar
  13. 13.
    Zhong, X.: Research on Video Semantic Feature Extraction Method. Huazhong University of Science and Technology, Wuhan (2013)Google Scholar
  14. 14.
    Zhong, X., Tao, H.: Image processing technology of flaws within infrared transmitting glasses. J. Wuhan Univ. Technol. 06, 1180–1183 (2012)CrossRefGoogle Scholar
  15. 15.
    Zhong, X., Wang, M., Zhang, G., Li, L.: A moving object detection method based on texture and color confidence fusion. Appl. Res. Comput. 07, 1–8 (2017)Google Scholar
  16. 16.
    Zhong, X., Yang, G., Lu, Y.: Key frame extraction method based on sub-lens segmentation and full connected graph with double threshold sliding window. Comput. Sci. 06, 289–293 (2016)Google Scholar
  17. 17.
    He, J., Guo, H., Li, L.: A dynamic video compression preprocessing algorithm based on visual color contrast sensitivity model. Appl. Res. Comput. 08, 2552–2556 (2016)Google Scholar
  18. 18.
    Wax, N.: Signal-to-noise improvement and the statistics of track populations. J. Appl. Phys. 26(5), 586–597 (1955)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Cheng, Y.: Mean shift, mode seeking, and clustering. IEEE Trans. Pattern Anal. Mach. Intell. 17(8), 790–799 (1995)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyWuhan University of TechnologyWuhanChina

Personalised recommendations