Skip to main content

Advertisement

Springer Nature Link
Log in

Online multi-view subspace learning via group structure analysis for visual object tracking

  • Published:
Distributed and Parallel Databases Aims and scope Submit manuscript

Abstract

In this paper, we focus on incrementally learning a robust multi-view subspace representation for visual object tracking. During the tracking process, due to the dynamic background variation and target appearance changing, it is challenging to learn an informative feature representation of tracking object, distinguished from the dynamic background. To this end, we propose a novel online multi-view subspace learning algorithm (OMEL) via group structure analysis, which consistently learns a low-dimensional representation shared across views with time changing. In particular, both group sparsity and group interval constraints are incorporated to preserve the group structure in the low-dimensional subspace, and our subspace learning model will be incrementally updated to prevent repetitive computation of previous data. We extensively evaluate our proposed OMEL on multiple benchmark video tracking sequences, by comparing with six related tracking algorithms. Experimental results show that OMEL is robust and effective to learn dynamic subspace representation for online object tracking problems. Moreover, several evaluation tests are additionally conducted to validate the efficacy of group structure assumption.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

Notes

  1. All the sequences can be downloaded from the following three web URLs: (1) http://www4.comp.polyu.edu.hk/~cslzhang/CT/CT.htm, (2) http://vision.ucsd.edu/~bbabenko/project_miltrack.html, (3) http://cv.snu.ac.kr/research/~vtd/.

References

  1. Adam, A., Rivlin, E., Shimshoni, I.: Robust fragments-based tracking using the integral histogram. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 798–805. IEEE (2006)

  2. Avidan, S.: Support vector tracking. IEEE Trans. Pattern Anal. Mach. Intell. 26(8), 1064–1072 (2004)

    Article  Google Scholar 

  3. Avidan, S.: Ensemble tracking. IEEE Trans. Pattern Anal. Mach. Intell. 29(2), 261–271 (2007)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  5. Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., Torr, P.H.: Fully-convolutional Siamese networks for object tracking. In: Proceedings of ECCV, pp. 850–865 (2016)

  6. Bolme, D.S., Beveridge, J.R., Draper, B.A., Lui, Y.M.: Visual object tracking using adaptive correlation filters. In: Proceedings of IEEE Conference on Computer Vision in Pattern Recognition, pp. 2544–2550 (2010)

  7. Chaudhuri, K., Kakade, S., Livescu, K., Sridharan, K.: Multi-view clustering via canonical correlation analysis. In: Proceedings of International Conference on Machine Learning, pp. 129–136 (2009)

  8. Chen, N., Zhu, J., Sun, F., Xing, E.P.: Large-margin predictive latent subspace learning for multi-view data analysis. IEEE Trans. Pattern Anal. Mach. Intell. 34(12), 2365–2378 (2012)

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. Danelljan, M., Bhat, G., Khan, F.S., Felsberg, M.: Eco: efficient convolution operators for tracking. In: Proceedings of IEEE Conference on Computer Vision in Pattern Recognition, pp. 21–26 (2017)

  11. Danelljan, M., Robinson, A., Khan, F.S., Felsberg, M.: Beyond correlation filters: learning continuous convolution operators for visual tracking. In: Proceedings of ECCV, pp. 472–488 (2016)

  12. Dehghan, A., Assari, S.M., Shah, M.: GMMCP tracker: globally optimal generalized maximum multi clique problem for multiple object tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2784–2791 (2015)

  13. Grabner, H., Grabner, M., Bischof, H.: Real-time tracking via on-line boosting. In: BMVC, pp. 47–56 (2006)

  14. Grabner, H., Leistner, C., Bischof, H.: Semi-supervised on-line boosting for robust tracking. In: Proceedings of ECCV, pp. 234–247. Springer, Heidelberg (2008)

  15. Guo, Y.: Convex subspace representation learning from multi-view data. In: AAAI Conference on Artificial Intelligence, pp. 387–393 (2013)

  16. Hare, S., Saffari, A., Torr, P.H.: Struck: structured output tracking with kernels. In: Proceedings of 13th IEEE International Conference on Computer Vision, pp. 263–270. IEEE (2011)

  17. He, J., Du, C., Zhuang, F., Yin, X., He, Q., Long, G.: Online Bayesian max-margin subspace multi-view learning. In: IJCAI, pp. 1555–1561 (2016)

  18. Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2015)

    Article  Google Scholar 

  19. Hsieh, C.J., Dhillon, I.S.: Fast coordinate descent methods with variable selection for non-negative matrix factorization. In: Proceedings of 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1064–1072. ACM (2011)

  20. Jepson, A.D., Fleet, D.J., El-Maraghi, T.F.: Robust online appearance models for visual tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(10), 1296–1311 (2003)

    Article  Google Scholar 

  21. Jordan, A.: On discriminative vs. generative classifiers: a comparison of logistic regression and naive bayes. In: Proceedings of Advances in Neural Information Processing Systems, pp. 841–848 (2002)

  22. Kwon, J., Lee, K.M.: Visual tracking decomposition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1269–1276. IEEE (2010)

  23. Lee, D.Y., Sim, J.Y., Kim, C.S.: Multihypothesis trajectory analysis for robust visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5088–5096. IEEE (2015)

  24. Liu, B., Huang, J., Kulikowski, C., Yang, L.: Robust visual tracking using local sparse appearance model and k-selection. IEEE Trans. Pattern Anal. Mach. Intell. 35(12), 2968–2981 (2013)

    Article  Google Scholar 

  25. Liwicki, S., Zafeiriou, S., Tzimiropoulos, G., Pantic, M.: Efficient online subspace learning with an indefinite kernel for visual tracking and recognition. IEEE Trans. Neural Netw. Learn. Syst. 23(10), 1624–1636 (2012)

    Article  Google Scholar 

  26. Mei, X., Ling, H.: Robust visual tracking using L1 minimization. In: Proceedings of 12th IEEE International Conference on Computer Vision, pp. 1436–1443. IEEE (2009)

  27. Nam, H., Han, B.: Learning multi-domain convolutional neural networks for visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4293–4302 (2016)

  28. Nebehay, G., Pflugfelder, R.: Clustering of static-adaptive correspondences for deformable object tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2784–2791. IEEE (2015)

  29. Ross, D.A., Lim, J., Lin, R.S., Yang, M.H.: Incremental learning for robust visual tracking. Int. J. Comput. Vis. 77(1), 125–141 (2008)

    Article  Google Scholar 

  30. Sharma, A., Kumar, A., Daume, H., Jacobs, D.W.: Generalized multiview analysis: a discriminative latent space. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2160–2167. IEEE (2012)

  31. Shi, Y., Li, W., Gao, Y., Cao, L., Shen, D.: Beyond IID: Learning to combine non-IID metrics for vision tasks. In: AAAI, pp. 1524–1531 (2017)

  32. Wang, H., Nie, F., Huang, H.: Multi-view clustering and feature learning via structured sparsity. In: Proceedings of 30th International Conference on Machine Learning, pp. 352–360 (2013)

  33. Wang, Y.X., Zhang, Y.J.: Nonnegative matrix factorization: a comprehensive review. IEEE Trans. Knowl. Data Eng. 25(6), 1336–1353 (2013)

    Article  Google Scholar 

  34. White, M., Yu, Y., Zhang, X., Schuurmans, D.: Convex multi-view subspace learning. In: Proceedings of Advances in Neural Information Processing Systems, pp. 1682–1690. Lake Tahoe, Nevada (2012)

  35. Wu, G., Kim, M., Sanroma, G., Wang, Q., Munsell, B.C., Shen, D., Initiative, A.D.N., et al.: Hierarchical multi-atlas label fusion with multi-scale feature representation and label-specific patch partition. NeuroImage 106, 34–46 (2015)

    Article  Google Scholar 

  36. Xiao, J., Stolkin, R., Leonardis, A.: Single target tracking using adaptive clustered decision trees and dynamic multi-level appearance models. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4978–4987. IEEE (2015)

  37. Xu, J., Ithapu, V.K., Mukherjee, L., Rehg, J.M., Singh, V.: Gosus: Grassmannian online subspace updates with structured-sparsity. In: Proceedings of 14th IEEE International Conference on Computer Vision, pp. 3376–3383. IEEE (2013)

  38. Yang, W., Gao, Y., Shi, Y., Cao, L.: MRM-Lasso: a sparse multi-view feature selection method via low-rank analysis. IEEE Trans. Neural Netw. Learn. Syst. 26(11), 2801–2815 (2015)

    Article  MathSciNet  Google Scholar 

  39. Zhang, C., Hu, Q., Fu, H., Zhu, P., Cao, X.: Latent multi-view subspace clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4279–4287 (2017)

  40. Zhang, K., Zhang, L., Yang, M.H.: Real-time object tracking via online discriminative feature selection. IEEE Trans. Image Process. 22(12), 4664–4677 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  41. Zhang, T., Bibi, A., Ghanem, B.: In defense of sparse tracking: Circulant sparse tracker. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3880–3888. IEEE (2016)

  42. Zhang, T., Liu, S., Xu, C., Yan, S., Ghanem, B., Ahuja, N., Yang, M.H.: Structural sparse tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 150–158. IEEE (2015)

  43. Zhang, T., Xu, C., Yang, M.H.: Multi-task correlation particle filter for robust object tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4819–4827 (2017)

Download references

Acknowledgements

This work is supported from the National Natural Science Foundation of China (Nos. 61603193, 61532006, 61432008, 61320106006), the Natural Science Foundation of Jiangsu Province (No. BK20171479), and Jiangsu Postdoctoral Science Foundation (No. 1701157B).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Nanjing Normal University, Nanjing, China

    Wanqi Yang & Ming Yang

  2. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Wanqi Yang, Yinghuan Shi & Yang Gao

Authors
  1. Wanqi Yang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Yinghuan Shi
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Yang Gao
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Ming Yang
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Wanqi Yang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, W., Shi, Y., Gao, Y. et al. Online multi-view subspace learning via group structure analysis for visual object tracking. Distrib Parallel Databases 36, 485–509 (2018). https://doi.org/10.1007/s10619-018-7227-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10619-018-7227-3

Keywords