Skip to main content
SpringerLink
Log in

Online object tracking by sparse and structural model

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

The objects tracking often meets the phenomenon, such as part or heavy occlusion, illumination variation, clutter background and scale variation and so on, during the tracking process, and which are the main challenge for object tracking. In this work, an online object tracking algorithm using local structural model with overlapped patches is proposed, the target is represented local structural feature with sparsity, furthermore, the sparse coefficients are pooled by the \(L_{2}\)-pooling method, and the target can be located more accurately and the occlusion problem can be better handled with this strategy, In addition, the paper develops an adaptive updating strategy based on increment subspace learning and sparse representation, this not only helps weaken the influence of illumination but also reduces the possibility of drifting. Numerous experiments demonstrate that the proposed algorithm performs more robustly and effectively against several state-of-the-art algorithms.

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
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. In: CSUR (2006)

  2. Smeulders, A.W.M., Chu, D.M., Cucchiara, R., Calderara, S., Dehghan, A., Shah, M.: Visual tracking: an experimental survey. IEEE Trans. Pattern Anal. Mach. Intell. 36, 1442-1468 (2014)

  3. Babenko, B., Yang, M.-H., Belongie, S.: Visual tracking with online multiple instance learning. In: CVPR, pp. 983–990 (2009)

  4. Kalal, Z., Matas, J., Mikolajczyk, K.: P-N learning: bootstrapping binary classifiers by structural constraints. In: CVPR, pp. 49–56 (2010)

  5. Grabner, H., Bischof, H.: On-line boosting and vison. In: CVPR, pp. 260–267 (2006)

  6. Wang, S., Lu, H., Yang, F., Yang, M.-H.: Superpixel tracking. In: ICCV, pp. 1323–1330 (2011)

  7. Ma, C., Huang, J.-B., Yang, X., Yang, M.-H.: Hierarchical convolutional features for visual tracking. In: ICCV, pp. 3074–3082 (2015)

  8. Liu, B., Huang, J., Yang, L., Kulikowsk, C.: Robust tracking using local sparse appearance model and k-selection. In: CVPR, pp. 1313–1320 (2011)

  9. Mei, X., Ling, H.: Robust visual tracking using \(l_1\) minimization. In: ICCV, pp. 1436–1443 (2009)

  10. Ross, D., Lim, J., Lin, R.-S., Yang, M-H.: Incremental learning for robust visual tracking. In: IJCV, pp. 125–141

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

  12. Han, G., Wang, X., Liu, J., Sun, N., Wang, C.: Robust object tracking based on local region sparse appearance model. Neurocomputing 184, 145–167 (2016)

  13. Wang, D., Lu, H., Yang, M.-H.: Online object tracking with sparse prototypes. IEEE Trans. Image Process. ITIP, 314–325 (2013)

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

  15. Dinh, T.B., Medioni, G.G.: Co-training framework of generative and discriminative trackers with partial occlusion handling. In: Proceeding of the IEEE Workshop on Applications of Computer Vision, pp. 642–649 (2011)

  16. Yu, Q., Dinh, T.B., Medioni, G.G.: Online tracking and reacquisition using co-training generative and discriminative trackers. In: ECCV, pp. 678–691 (2008)

  17. Mattews, I., Ishikawa, T., Baker, S.: The template update problem. In: PAMI, pp. 810–815 (2004)

  18. Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. In: PAMI, pp. 210–227 (2009)

  19. Wright, J., Ma, Y., Mairal, J., Sapiro, G., Huang, T.S., Yan, S.: Sparse representation for computer vision and pattern recognition. In: Proceedings of the IEEE, pp. 1031–1044 (2010)

  20. Liu, B., Yang, L., Huang, J., Meer, P., Gong, L., Kulikowski, C.: Robust and fast collaborative tracking with two stage sparse optimization. In: ECCV, pp. 624–637 (2010)

  21. Zhou, Z., Zhou, M., LI, W.H.: Object tracking algorithm based on hybrid particle filter and sparse representation. Pattern Recognit. Artif. Intell. https://doi.org/10.16451/j.cnki.issn1003-6059.201601003 (2016)

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

  23. Yang, J., Yu, K., Gong, Y., Huang, T.: Linear spatial pyramid matching using sparse coding for image classification. In: CVPR, pp. 1794–1801 (2009)

Download references

Acknowledgements

This work was sponsored by National Natural Science Foundation of China (61503329). Prospective Joint Research Project of Jiangsu Province (BY201506-01).

Author information

Authors and Affiliations

  1. Department of Automation, College of Information Engineering, Yangzhou University, Yangzhou, 225009, People’s Republic of China

    Zhibo Guo, Kejun Lin, Jian Huang, Ying Zhang & Zhengda Cui

Authors
  1. Zhibo Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kejun Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jian Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ying Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhengda Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhibo Guo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, Z., Lin, K., Huang, J. et al. Online object tracking by sparse and structural model. Cluster Comput 22 (Suppl 2), 2801–2808 (2019). https://doi.org/10.1007/s10586-017-1527-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-1527-7

Keywords

Search

Navigation