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Structured Visual Tracking with Dynamic Graph

  • Zhaowei Cai
  • Longyin Wen
  • Jianwei Yang
  • Zhen Lei
  • Stan Z. Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7726)

Abstract

Structure information has been increasingly incorporated into computer vision field, whereas only a few tracking methods have employed the inner structure of the target. In this paper, we introduce a dynamic graph with pairwise Markov property to model the structure information between the inner parts of the target. The target tracking is viewed as tracking a dynamic undirected graph whose nodes are the target parts and edges are the interactions between parts. These target parts within the graph waiting for matching are separated from the background with graph cut, and a spectral matching technique is exploited to accomplish the graph tracking. With the help of an intuitive updating mechanism, our dynamic graph can robustly adapt to the variations of target structure. Experimental results demonstrate that our structured tracker outperforms several state-of-the-art trackers in occlusion and structure deformations.

Keywords

Color Histogram Visual Tracking Conditional Random Field Graph Match Dynamic Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Lim, J., Ross, D.A., Lin, R.S., Yang, M.H.: Incremental learning for visual tracking. In: NIPS (2004)Google Scholar
  2. 2.
    Wang, S., Lu, H., Yang, F., Yang, M.H.: Superpixel tracking. In: ICCV, pp. 1323–1330 (2011)Google Scholar
  3. 3.
    Grabner, H., Bischof, H.: On-line boosting and vision. In: CVPR, vol. (1), pp. 260–267 (2006)Google Scholar
  4. 4.
    Babenko, B., Yang, M.H., Belongie, S.: Robust object tracking with online multiple instance learning. IEEE Trans. Pattern Anal. Mach. Intell. 33(8), 1619–1632 (2011)CrossRefGoogle Scholar
  5. 5.
    Tian, M., Zhang, W., Liu, F.: On-Line Ensemble SVM for Robust Object Tracking. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) ACCV 2007, Part I. LNCS, vol. 4843, pp. 355–364. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Mei, X., Ling, H.: Robust visual tracking using ℓ1 minimization. In: ICCV, pp. 1436–1443 (2009)Google Scholar
  7. 7.
    Liu, B., Huang, J., Yang, L., Kulikowski, C.A.: Robust tracking using local sparse appearance model and k-selection. In: CVPR, pp. 1313–1320 (2011)Google Scholar
  8. 8.
    Kwon, J., Lee, K.M.: Tracking of a non-rigid object via patch-based dynamic appearance modeling and adaptive basin hopping monte carlo sampling. In: CVPR, pp. 1208–1215 (2009)Google Scholar
  9. 9.
    Adam, A., Rivlin, E., Shimshoni, I.: Robust fragments-based tracking using the integral histogram. In: CVPR, vol. (1), pp. 798–805 (2006)Google Scholar
  10. 10.
    Ren, X., Malik, J.: Tracking as repeated figure/ground segmentation. In: CVPR (2007)Google Scholar
  11. 11.
    Felzenszwalb, P.F., Girshick, R.B., McAllester, D.A., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)CrossRefGoogle Scholar
  12. 12.
    Quattoni, A., Collins, M., Darrell, T.: Conditional random fields for object recognition. In: NIPS (2004)Google Scholar
  13. 13.
    Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1124–1137 (2004)CrossRefGoogle Scholar
  14. 14.
    Kalal, Z., Matas, J., Mikolajczyk, K.: P-N learning: Bootstrapping binary classifiers by structural constraints. In: CVPR, pp. 49–56 (2010)Google Scholar
  15. 15.
    Shahed, S.M.N., Ho, J., Yang, M.H.: Online visual tracking with histograms and articulating blocks. Computer Vision and Image Understanding 114(8), 901–914 (2010)CrossRefGoogle Scholar
  16. 16.
    Yang, M., Wu, Y., Lao, S.: Intelligent collaborative tracking by mining auxiliary objects. In: CVPR, vol. (1), pp. 697–704 (2006)Google Scholar
  17. 17.
    Tsai, D., Flagg, M., Rehg, J.M.: Motion coherent tracking with multi-label mrf optimization. In: BMVC, pp. 1–11 (2010)Google Scholar
  18. 18.
    Grundmann, M., Kwatra, V., Han, M., Essa, I.A.: Efficient hierarchical graph-based video segmentation. In: CVPR, pp. 2141–2148 (2010)Google Scholar
  19. 19.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Ssstrunk, S.: SLIC Superpixels. Technical report, EPFL (2010)Google Scholar
  20. 20.
    Bordes, A., Ertekin, S., Weston, J., Bottou, L.: Fast kernel classifiers with online and active learning. Journal of Machine Learning Research 6, 1579–1619 (2005)MathSciNetzbMATHGoogle Scholar
  21. 21.
    Leordeanu, M., Hebert, M.: A spectral technique for correspondence problems using pairwise constraints. In: ICCV, pp. 1482–1489 (2005)Google Scholar
  22. 22.
    Santner, J., Leistner, C., Saffari, A., Pock, T., Bischof, H.: Prost: Parallel robust online simple tracking. In: CVPR, pp. 723–730 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Zhaowei Cai
    • 1
  • Longyin Wen
    • 1
  • Jianwei Yang
    • 1
  • Zhen Lei
    • 1
  • Stan Z. Li
    • 1
    • 2
  1. 1.CBSR & NLPR, Institute of AutomationChinese Academy of SciencesChina
  2. 2.China Research and Development Center for Internet of ThingChinese Academy of SciencesChina

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