Skip to main content
SpringerLink
Log in

Multi-scale patch-based sparse appearance model for robust object tracking

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

When objects undergo large pose change, illumination variation or partial occlusion, most existing visual tracking algorithms tend to drift away from targets and even fail to track them. To address the issue, in this paper we propose a multi-scale patch-based appearance model with sparse representation and provide an efficient scheme involving the collaboration between multi-scale patches encoded by sparse coefficients. The key idea of our method is to model the appearance of an object by different scale patches, which are represented by sparse coefficients with different scale dictionaries. The model exploits both partial and spatial information of targets based on multi-scale patches. Afterwards, a similarity score of one candidate target is input into a particle filter framework to estimate the target state sequentially over time in visual tracking. Additionally, to decrease the visual drift caused by frequently updating model, we present a novel two-step object tracking method which exploits both the ground truth information of the target labeled in the first frame and the target obtained online with the multi-scale patch information. Experiments on some publicly available benchmarks of video sequences showed that the similarity involving complementary information can locate targets more accurately and the proposed tracker is more robust and effective than others.

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

Similar content being viewed by others

References

  1. Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Survey 38(4), 1–45 (2006)

  2. Wright, J., Ma, Y., Mairal, J., Sapiro, G., Huang, T., Yan, S.: Sparse representation for computer vision and pattern recognition. Proc. IEEE. 98(6), 1031–1044 (2010)

    Article  Google Scholar 

  3. Candès, E., Romberg, J., Tao, T.: Stable signal recovery from incomplete and inaccurate measurements. Comm. Pure Appl. Math. 59(8), 1207–1223 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  4. Donoho, D.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  5. Mei, X., Ling, H.: Robust visual tracking using \(l_{1}\) minimization, ICCV, pp. 1436–1443 (2009)

  6. Li, H.X., Shen, C.: Robust real-time visual tracking using compressive sensing. CVPR, pp. 1305–1312 (2011)

  7. Han, Z., Jiao, J., Zhang, B., Ye, Q., Liu, J.: Visual object tracking via sample-based adaptive sparse representation (AdaSR). Pattern Recogn. 44(9), 2170–2183 (2011)

  8. Bai, T., Li, Y.F.: Robust visual tracking with structured sparse representation appearance model. Pattern Recogn. 45(6), 2390–2404 (2012)

    Article  MATH  Google Scholar 

  9. Liu, B., Huang, J. et al.: Robust tracking using local sparse appearance model and K-selection. CVPR, pp. 1313–1320 (2011)

  10. Jia, X., Lu, H., Yang, M.H.: Visual tracking via adaptive structural local sparse appearance model. CVPR, pp. 1822–1829 (2012)

  11. Matthews, I., Ishikawa, T., Baker, S.: The template update problem, PAMI. 810–815 (2004)

  12. Wang, Q., Chen, F., Xu, W., Yang, M.-H.: Object tracking via partial least squares analysis. IEEE Trans. Image Process. 21(10), 4454–4465 (2012)

    Article  MathSciNet  Google Scholar 

  13. Pérez, P., Hue, C. Vermaak, J., Gangnet, M.: Color-based probabilistic tracking. In: Proceedings of European conference on computer vision, pp. 661–675 (2002)

  14. Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–575 (2003)

    Article  Google Scholar 

  15. Ross, D., Lim, J., Lin, R.S., Yang, M.-H.: Incremental learning for robust visual tracking. IJCV 77(1), 125–141 (2008)

    Article  Google Scholar 

  16. Kwon, J., Lee, K. M.: Visual tracking decomposition. CVPR, pp. 1269–1276 (2010)

  17. Kwon, J., Lee, K.M.: Tracking by sampling trackers. ICCV, pp. 1195–1202 (2011)

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

    Article  Google Scholar 

  19. Ramirez-Paredes, J.-P., Sanchez-Yanez, R.E., Ayala-Ramirez, V.: A fuzzy inference approach to template-based visual tracking. Mach. Vis. Appl. 23(3), 427–439 (2012)

  20. Wang, D., Lu, H.C., Yang, M.-H.: Online Object Tracking with sparse prototypes. IEEE Trans. Image Process. 22(1), 314–325 (2013)

    Article  MathSciNet  Google Scholar 

  21. Zhong, W., Lu, H.C., Yang, M.-H.: Robust object tracking via sparsity-based collaborative model. In CVPR, pp. 1838–1845 (2012)

  22. Wu, Y., Cheng, J., Wang, J.Q., Lu, H.Q., Wang, J., Ling, H.B., Blasch, E., Bai, L.: Real-time probabilistic covariance tracking with efficient model update. IEEE Trans. Image Process. 21(5), 2824–2837 (2012)

    Article  MathSciNet  Google Scholar 

  23. Avidan, S.: Ensemble tracking. PAMI 29(2), 261–271 (2007)

    Article  Google Scholar 

  24. Viola, P., Platt, J.C., Zhang, C.: Multiple instance boosting for object detection. In: NIPS. 1417–1426 (2005)

  25. Avidan, S.: Support vector tracking. In: CVPR. pp. 184-191 (2001)

  26. Grabner, H., Bischof, H.: On-line boosting and vision. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 260–267 (2006)

  27. Parag, T., Porikli, F., Elgammal, A.: Boosting adaptive linear weak classifiers for online learning and tracking. CVPR. pp. 1–8 (2008)

  28. Carvalho, P., Oliveira, T., Ciobanu, L., Gaspar, F., et al.: Analysis of object description methods in a video object tracking environment. Mach. Vis. Appl. 24(6), 1149–1165 (2011)

    Article  Google Scholar 

  29. 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)

  30. Wright, J., Yang, A., Ganesh, A., Sastry, S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2008)

  31. Chen, F., Wang, Q., Wang, S., Zhang, W., Xu, W.: Object tracking via appearance modeling and sparse representation. Image Vis. Comput. 21(11), 787–796 (2011)

  32. Wang, Q., Chen, F., Xu, W., Yang, M.-H.: Online discriminative object tracking with local sparse representation’, WACV ’12 Proceedings of the 2012 IEEE Workshop on the Applications of Computer Vision, pp. 425–432 (2012)

  33. Doucet, A., de Freitas, N., Gordon, N.: Sequential Monte Carlo Methods in Practice. Springer, New York (2001)

  34. Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Trans. Signal Process. 50(2), 174–188 (2002)

    Article  Google Scholar 

  35. Zhang K.H., Zhang L., Yang M.H.: Real-time compressive tracking. ECCV, pp. 864–877 (2012)

  36. http://www.dabi.temple.edu/~hbling/code_data.htm

  37. http://www.cs.toronto.edu/~dross/ivt/

  38. http://faculty.ucmerced.edu/mhyang/pubs.html

  39. Tropp, J.A., Gilbert, A.C.: Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inf. Theory 53(12), 4655–4666 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  40. Everingham, M., Gool, L.V., Williams, C., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. IJCV 88(2), 303–338 (2010)

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the NSFC-Guangdong Joint Foundation Key Project under Grant (No. U1135003), the National Nature Science Foundation of China (Nos. 61070227, 61300058 and 41302261).

Author information

Authors and Affiliations

  1. School of Computer and Information, Hefei University of Technology, Hefei, 230009, China

    Chengjun Xie, Jieqing Tan & Lei He

  2. Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, 230031, China

    Chengjun Xie & Jie Zhang

  3. Institute of Health Sciences, Anhui University, Hefei, 230601, Anhui, China

    Peng Chen

Authors
  1. Chengjun Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jieqing Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jie Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lei He
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peng Chen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xie, C., Tan, J., Chen, P. et al. Multi-scale patch-based sparse appearance model for robust object tracking. Machine Vision and Applications 25, 1859–1876 (2014). https://doi.org/10.1007/s00138-014-0632-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-014-0632-3

Keywords

Search

Navigation