A Hybrid Real-Time Visual Tracking Using Compressive RGB-D Features

  • Mengyuan Zhao
  • Heng Luo
  • Ahmad P. Tafti
  • Yuanchang Lin
  • Guotian He
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9474)


The online multi-instance learning tracking (MIL) algorithm is known for its ability of alleviating tracking drift by training classifiers with positive and negative bag. However, the increased computational complexity results in time consuming due to the lack of consideration of sampling importance when collecting training samples. Additionally, the MIL method, as a 2D feature-based tracking algorithm, performs unsteadily when the object changes poses or rotates seriously. In this paper, a histogram-based feature similarity measurement is employed as a weighting strategy to select positive samples. Benefited from profitable depth information, the tracking algorithm we proposed achieves higher tracking performance. For computational efficiency, a compressive sensing method is adopted to extract features and reduce dimensionality. Experimental results demonstrate that our algorithm is better in robustness, accuracy, efficiency than three state-of-the-art methods on challenging video sequences.


Visual tracking MIL Histogram feature similarity Depth feature Compressive sensing 


  1. 1.
    Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. 38, 13 (2006)CrossRefGoogle Scholar
  2. 2.
    Collins, R.T., Liu, Y., Leordeanu, M.: Online selection of discriminative tracking features. IEEE Trans. Pattern Anal. Mach. Intell. 27, 1631–1643 (2005)CrossRefGoogle Scholar
  3. 3.
    Grabner, H., Bischof, H.: On-line boosting and vision. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 260–267 (2006)Google Scholar
  4. 4.
    Grabner, H., Grabner, M., Bischof, H.: Real-time tracking via on-line boosting. BMVC 1, 6 (2006)Google Scholar
  5. 5.
    Avidan, S.: Ensemble tracking. IEEE Trans. Pattern Anal. Mach. Intell.29, 261–271 (2007)CrossRefGoogle Scholar
  6. 6.
    Babenko, B., Yang, M.H., Belongie, S.: Robust object tracking with online multiple instance learning. IEEE Trans. Pattern Anal. Mach. Intell.38, 1619–1632 (2011)CrossRefGoogle Scholar
  7. 7.
    Zhang, K., Zhang, L., Yang, M.-H.: Real-time compressive tracking. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 864–877. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  8. 8.
    Babenko, B., Yang, M.H., Belongie, S.: Visual tracking with online multiple instances learning. In: IEEE Conference on Computer Vision and Pattern Recognition 2009, CVPR 2009, pp. 983–990. IEEE (2009)Google Scholar
  9. 9.
    Zhang, K., Song, H.: Real-time visual tracking via online weighted multiple instance learning. Pattern Recogn. 46(January), 397–411 (2013)zbMATHCrossRefGoogle Scholar
  10. 10.
    Zhang, K., Zhang, L., Yang, M.-H.: Fast compressive tracking. IEEE Trans. Pattern Anal. Mach. Intell. 36(10), 2002–2015 (2014)CrossRefGoogle Scholar
  11. 11.
    Guo, C.: Research on Online Video Object Tracking Algorithm in Presence of Its Zoom and Occlusions. Chongqing University, Chongqing (2014)Google Scholar
  12. 12.
    Smeaton, A.F., O’Connor, N.E.: An improved spatiogram similarity measure for robust object localization. In: Proceedings of ICASSP, pp. 1067–1072. IEEE, Honolulu (2007)Google Scholar
  13. 13.
    Candes, E., Tao, T.: Decoding by linear programming. IEEE Trans. Inf. Theor. 51(12), 4203–4215 (2005)zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Candes, E., Tao, T.: Near-optimal signal recovery from random projections: universal encoding strategies. IEEE Trans. Inf. Theor. 52(12), 5406–5425 (2006)zbMATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 511–518 (2001)Google Scholar
  16. 16.
    Li, H., Shen, C., Shi, Q.: Real-time visual tracking using compressive sensing. In: CVPR, pp. 1305–1312 (2011)Google Scholar
  17. 17.
    Wright, J., Yang, A., Ganesh, A., Sastry, S., Ma, Y.: Robust face recognition viasparse representation. PAMI 31, 210–227 (2009)CrossRefGoogle Scholar
  18. 18.
    Liu, L., Fieguth, P.: Texture classic cation from random features. PAMI 34, 574–586 (2012)CrossRefGoogle Scholar
  19. 19.
    Achlioptas, D.: Database-friendly random projections: Johnson-Lindenstrauss with binary coins. J. Comput. Syst. Sci. 66(4), 671–687 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  20. 20.
    Bingham, E., Mannila, H.: Random projection in dimensionality reduction: applications to image and text data. In: International Conference on Knowledge Discovery and Data Mining, pp. 245–250 (2001)Google Scholar
  21. 21.
    Baraniuk, R., Davenport, M., DeVore, R., Wakin, M.: A simple proof of the restricted isometry property for random matrices. Constr. Approx. 28, 253–263 (2008)zbMATHCrossRefMathSciNetGoogle Scholar
  22. 22.
    Achlioptas, D.: Database-friendly random projections: Johnson-Lindenstrauss with binary coins. J. Comput. Syst. Sci. 66, 671–687 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  23. 23.
    Li, P., Hastie, T., Church, K.: Very sparse random projections. In: KDD, pp. 287–296 (2006)Google Scholar
  24. 24.
    Baraniuk, R.: Compressive sensing. IEEE Sig. Process. Mag. 24(4), 118–121 (2007)CrossRefGoogle Scholar
  25. 25.
    Wu, Y., Jia, N., Sun, J.: Real-time multi-scale tracking based on compressive sensing. Original Article 30(4) (2014)Google Scholar
  26. 26.
    Wu, Y., Lim, J., Yang, M.-H.: Online object tracking: a benchmark. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2411–2418 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Mengyuan Zhao
    • 1
  • Heng Luo
    • 2
  • Ahmad P. Tafti
    • 3
  • Yuanchang Lin
    • 4
  • Guotian He
    • 4
  1. 1.College of Computer Science and TechnologyChongqing University of Posts and TelecommunicationsChongqingChina
  2. 2.College of Communication and Information EngineeringNanjing University of Posts and TelecommunicationsNanjingChina
  3. 3.Department of Computer ScienceUniversity of Wisconsin-MilwaukeeMilwaukeeUSA
  4. 4.Chongqing Institute of Green and Intelligent Technology, Chinese Academy of SciencesChongqingChina

Personalised recommendations