Skip to main content
SpringerLink
Log in

Visual tracking using IPCA and sparse representation

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

The main challenging issues in visual tracking can be listed as follows: significant variation of object’s appearance or background image, illumination changes, serious or even complete occlusion of object, etc. In order to deal with them, two modules are needed: one is an accurate appearance model updating online and the other one is a robust matching method to find the target according to the learned model. In this paper, we propose a novel tracking method in a particle filter framework based on IPCA and sparse representation, in which IPCA is used to model the object appearance adaptively and sparse representation is used in two aspects: first, it helps to formulate a robust updating scheme of the IPCA; second, it strengthens the matching process significantly when the tracker copes with very challenging cases as mentioned in the beginning. In experiments, we select three state-of-the-art tracking methods for comparison and demonstrate the superiority of our method over them on various data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Abbreviations

\({{TI}_{i}}\) :

The \({i}_{th}\) training images.

\(\bar{TI}\) :

Mean value of \(\{{{TI}_{1}},{{TI}_{2}},\ldots ,{{TI}_{n}}\}\).

A:

\(d\times n\) data matrix (\([{{TI}_{1}},{{TI}_{2}},\ldots ,{{TI}_{n}}]\)).

B:

\(d\times m\) data matrix (\([{{TI}_{n+1}},\ldots ,{{TI}_{n+m}}]\)).

\(U\varSigma {{V}^{\mathrm{T}}}\) :

Singular value decomposition.

\(t_f\) :

Fixed target template.

\({t_d}^{0}\) :

Original dynamical target template.

\(t_d\) :

Dynamical target template.

\(f\) :

Forgetting factor.

\(n\) :

Number of frames processed.

\(m\) :

Number of frames newly come.

\(\bar{V}_m\) :

Mean value of the targets in \(m\) frames.

\(i_i\) :

One trivial template.

\(I\!=\!{[}{i}_{1},{i}_{2},{\ldots },{i}_{l}{]}\) :

Identity matrix.

\(T\!=\!{[}{{t}_{d}},{{t}_{f}}{]}\) :

Target template set.

\(y\) :

Tracking result.

\(a\!=\!{[}{{a}_{1}},{{a}_{2}}{]}^{T}\) :

Target coefficient vector.

\(e\!=\!{{[}{{e}_{1}},\ldots ,{{e}_{l}}{]}^{T}}\) :

Trivial coefficient vector.

\(\varepsilon \) :

Error vector.

\({{e}^{+}}\) :

Positive trivial coefficient vector.

\({{e}^{-}}\) :

Negative trivial coefficient vector.

\(c\) :

Nonnegative coefficient vector.

\(y_0\) :

Tracking result in the previous frame.

\(t\) :

Time in particle filter framework.

\({{X}_{t}}\) :

Parameter of affine transformation.

\(\mu \) :

Center of the subspace spanned by \(U\).

\(d_t\) :

Distance to the subspace.

\(d_w\) :

Distance to the subspace center.

References

  1. Ahn, J.H., Choi, S., Oh, J.H.: A new way of PCA: integrated-squared-error and EM algorithms. In: Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP ’04). IEEE International Conference on, vol. 5, pp. V-777–V-780 (2004). doi:10.1109/ICASSP.2004.1327226

  2. Babenko, B., Yang, M.H., Belongie, S.: Visual tracking with online multiple instance learning. In: CVPR 2009. IEEE Conference on, pp. 983–990 (2009). doi:10.1109/CVPR.2009.5206737

  3. Babenko, B., Yang, M.H., Belongie, S.: Robust object tracking with online multiple instance learning. Pattern Anal. Mach. Intell. IEEE Trans. 33(8), 1619–1632 (2011). doi:10.1109/TPAMI.2010.226

    Article  Google Scholar 

  4. Berclaz, J., Fleuret, F., Fua, P.: Robust people tracking with global trajectory optimization. In: Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, vol. 1, pp. 744–750 (2006). doi:10.1109/CVPR.2006.258

  5. Candes, E.J., Romberg, J.K., Tao, T.: Stable signal recovery from incomplete and inaccurate measurements. Commun. Pure Appl. Math. 59(8), 1207–1223 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  6. Collins, R.: Mean-shift blob tracking through scale space. In: Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on, vol. 2, pp. II-234–II-240 (2003). doi:10.1109/CVPR.2003.1211475

  7. Collins, R., Liu, Y., Leordeanu, M.: Online selection of discriminative tracking features. Pattern Anal. Mach. Intell. IEEE Trans. 27(10), 1631–1643 (2005). doi:10.1109/TPAMI.2005.205

    Article  Google Scholar 

  8. Ferryman, J., Crowley, J.L.: Crowd-pets09. http://www.cvg.cs.rdg.ac.uk/PETS2001/ (2009)

  9. Dagher, I., Nachar, R.: Face recognition using IPCA-ICA algorithm. Pattern Anal. Mach. Intell. IEEE Trans. 28(6), 996–1000 (2006). doi:10.1109/TPAMI.2006.118

    Article  Google Scholar 

  10. Decker, D., Punch, W., Sticklen, J.: IPCA, an architecture for intelligent control. In: Intelligent Control, 1996, Proceedings of the 1996 IEEE International Symposium on, pp. 80–85 (1996). doi:10.1109/ISIC.1996.556181

  11. Everingham, M., Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The Pascal visual object classes (voc) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010). doi:10.1007/s11263-009-0275-4.

    Google Scholar 

  12. Gu, S., Zheng, Y., Tomasi, C.: Linear time offline tracking and lower envelope algorithms. In: Computer Vision (ICCV), 2011 IEEE International Conference on, pp. 1840–1846 (2011). doi:10.1109/ICCV.2011.6126451

  13. Hargrave, P.: A tutorial introduction to Kalman filtering. In: Kalman Filters: Introduction, Applications and Future Developments, IEE Colloquium on, pp. 1/1–1/6 (1989)

  14. Herman, M., Strohmer, T.: General deviants: an analysis of perturbations in compressed sensing. Sel. Top. Signal Process. IEEE J. 4(2), 342–349 (2010). doi:10.1109/JSTSP.2009.2039170

    Article  Google Scholar 

  15. Kalal, Z., Matas, J., Mikolajczyk, K.: Online learning of robust object detectors during unstable tracking. In: Computer Vision Workshops (ICCV Workshops), 2009 IEEE 12th International Conference on, pp. 1417–1424 (2009). doi:10.1109/ICCVW.2009.5457446

  16. Kalal, Z., Matas, J., Mikolajczyk, K.: P-n learning: Bootstrapping binary classifiers by structural constraints. In: Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pp. 49–56 (2010). doi:10.1109/CVPR.2010.5540231

  17. Kim, S.J., Koh, K., Lustig, M., Boyd, S., Gorinevsky, D.: An interior-point method for large-scale l1-regularized least squares. Sel. Top. Signal Process. IEEE J. 1(4), 606–617 (2007). doi:10.1109/JSTSP.2007.910971

    Article  Google Scholar 

  18. Leibe, B., Schindler, K., Van Gool, L.: Coupled detection and trajectory estimation for multi-object tracking. In: Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on, pp. 1–8 (2007). doi:10.1109/ICCV.2007.4408936

  19. Levy, A., Lindenbaum, M.: Sequential karhunen-loeve basis extraction and its application to images. In: Image Processing, 1998. ICIP 98. Proceedings. 1998 International Conference on, vol. 2, pp. 456–460 (1998). doi:10.1109/ICIP.1998.723422

  20. Liu, B., Ji, C., Zhang, Y., Hao, C., Wong, K.K.: Multi-target tracking in clutter with sequential monte carlo methods. IET Radar, Sonar Navig. 4(5), 662–672 (2010). doi:10.1049/iet-rsn.2009.0051

    Article  Google Scholar 

  21. Liu, L., Fieguth, P.: Texture classification from random features. Pattern Anal. Mach. Intell. IEEE Trans. 34(3), 574–586 (2012). doi:10.1109/TPAMI.2011.145

    Article  Google Scholar 

  22. Mei, X., Ling, H.: Robust visual tracking and vehicle classification via sparse representation. Pattern Anal. Mach. Intell. IEEE Trans. 33(11), 2259–2272 (2011). doi:10.1109/TPAMI.2011.66

    Article  Google Scholar 

  23. Ross, D.A., Lim, J., Lin, R.S., Yang, M.H.: Incremental learning for robust visual tracking. Int. J. Comput. Vis. 77(1–3), 125–141 (2008). doi:10.1007/s11263-007-0075-7.

    Google Scholar 

  24. Wright, J., Yang, A., Ganesh, A., Sastry, S., Ma, Y.: Robust face recognition via sparse representation. Pattern Anal. Mach. Intell., IEEE Trans. 31(2), 210–227 (2009). doi:10.1109/TPAMI.2008.79

    Article  Google Scholar 

  25. Yang, C., Duraiswami, R., Davis, L.: Efficient mean-shift tracking via a new similarity measure. In: Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, vol. 1, pp. 176–183 (2005). doi:10.1109/CVPR.2005.139

  26. Zhang, D., Van Gool, L., Oosterlinck, A.: Generalized predictive control of a vision-based tracking system using kalman filtering technique. In: Control and Applications, 1989. Proceedings. ICCON ’89. IEEE International Conference on, pp. 731–733 (1989). doi:10.1109/ICCON.1989.770616

  27. Zhou, H., Yuan, Y., Shi, C.: Object tracking using sift features and mean shift. Comput. Vis. Image Underst. 113(3), 345–352 (2009). doi:10.1016/j.cviu.2008.08.006

    Google Scholar 

  28. Zhou, S.K., Chellappa, R., Moghaddam, B.: Visual tracking and recognition using appearance-adaptive models in particle filters. Image Process. IEEE Trans. 13(11), 1491–1506 (2004). doi:10.1109/TIP.2004.836152

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by National Key Basic Research Project of China (973Program) 2011CB302400 and National Nature Science Foundation of China (NSFC Grant No. 61071156).

Author information

Authors and Affiliations

  1. Key Laboratory of Machine Perception (MOE), Peking University, Beijing, 100871, China

    Dongjing Shan & Chao Zhang

Authors
  1. Dongjing Shan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dongjing Shan.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shan, D., Zhang, C. Visual tracking using IPCA and sparse representation. SIViP 9, 913–921 (2015). https://doi.org/10.1007/s11760-013-0525-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-013-0525-3

Keywords

Search

Navigation