Skip to main content
Springer Nature Link
Log in

Learning Noise-Aware Correlation Filter for Visual Tracking

  • Conference paper
  • First Online:
Big Data (Big Data 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 945))

Included in the following conference series:

  • 1958 Accesses

Abstract

Correlation filter has recently attracted much attention in visual tracking due to their excellent performance on both accuracy and efficiency. However, the adopted features, such as Colors, HOG and deep features, usually include noises and/or corruptions which might disturb the tracking performance. To handle this problem, we propose a novel noise-aware correlation filter method for robust visual tracking. In particular, we decompose the input feature matrix into a “clean” feature matrix and a sparse noise matrix, and then use the “clean” feature to train the correlation filter. To optimize the proposed correlation filter, we design an efficient ADMM (alternation direction of multipliers) solver. Extensive experimental results on the OTB-2013 dataset show that the proposed approach performs favorably against state-of-the-art trackers.

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

Access this chapter

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

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. 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 and Pattern Recognition, pp. 2544–2550. IEEE (2010)

    Google Scholar 

  2. Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends® Mach. Learn. 3(1), 1–122 (2011)

    MATH  Google Scholar 

  3. Candès, E.J., Li, X., Ma, Y., Wright, J.: Robust principal component analysis? J. ACM (JACM) 58(3), 11 (2011)

    Article  MathSciNet  Google Scholar 

  4. Chen, M., Ganesh, A., Lin, Z., Ma, Y., Wright, J., Wu, L.: Fast convex optimization algorithms for exact recovery of a corrupted low-rank matrix. Coordinated Science Laboratory Report no. UILU-ENG-09-2214 (2009)

    Google Scholar 

  5. Danelljan, M., Hager, G., Khan, F., Felsberg, M.: Accurate scale estimation for robust visual tracking. In: Proceedings of British Machine Vision Conference. BMVA Press (2014)

    Google Scholar 

  6. Danelljan, M., Bhat, G., Khan, F.S., Felsberg, M.: ECO: efficient convolution operators for tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. IEEE (2017)

    Google Scholar 

  7. Danelljan, M., Hager, G., Shahbaz Khan, F., Felsberg, M.: Learning spatially regularized correlation filters for visual tracking. In: Proceedings of IEEE International Conference on Computer Vision, pp. 4310–4318. IEEE (2015)

    Google Scholar 

  8. Danelljan, M., Robinson, A., Shahbaz Khan, F., Felsberg, M.: Beyond correlation filters: learning continuous convolution operators for visual tracking. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 472–488. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46454-1_29

    Chapter  Google Scholar 

  9. Danelljan, M., Shahbaz Khan, F., Felsberg, M., Van de Weijer, J.: Adaptive color attributes for real-time visual tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1090–1097. IEEE (2014)

    Google Scholar 

  10. Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)

    Article  Google Scholar 

  11. Galoogahi, H.K., Fagg, A., Lucey, S.: Learning background-aware correlation filters for visual tracking. In: Proceedings of IEEE Conference on Computer Vision, pp. 1144–1152. IEEE (2017)

    Google Scholar 

  12. Gray, R.M.: Toeplitz and circulant matrices: a review. Found. Trends® Commun. Inf. Theory 2(3), 155–239 (2006)

    Article  Google Scholar 

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

    Google Scholar 

  14. Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: Exploiting the circulant structure of tracking-by-detection with kernels. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7575, pp. 702–715. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33765-9_50

    Chapter  Google Scholar 

  15. 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 

  16. Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1409–1422 (2012)

    Article  Google Scholar 

  17. Li, C., Cheng, H., Hu, S., Liu, X., Tang, J., Lin, L.: Learning collaborative sparse representation for grayscale-thermal tracking. IEEE Trans. Image Process. 25(12), 5743–5756 (2016)

    Article  MathSciNet  Google Scholar 

  18. Li, C., Liang, X., Lu, Y., Zhao, N., Tang, J.: RGB-T object tracking: benchmark and baseline. arXiv preprint arXiv:1805.08982 (2018)

  19. Li, C., Lin, L., Zuo, W., Tang, J.: Learning patch-based dynamic graph for visual tracking. In: Proceedings of The AAAI Conference on Artificial Intelligence, pp. 4126–4132. AAAI (2017)

    Google Scholar 

  20. Li, C., Lin, L., Zuo, W., Tang, J., Yang, M.H.: Visual tracking via dynamic graph learning. IEEE TPAMI (2018). https://doi.org/10.1109/TPAMI.2018.2864965

  21. Li, C., Wu, X., Bao, Z., Tang, J.: ReGLe: spatially regularized graph learning for visual tracking. In: Proceedings of the ACM on Multimedia Conference, pp. 252–260. ACM (2017)

    Google Scholar 

  22. Li, C., Zhao, N., Lu, Y., Zhu, C., Tang, J.: Weighted sparse representation regularized graph learning for RGB-T object tracking. In: Proceedings of the ACM on Multimedia Conference, pp. 1856–1864. ACM (2017)

    Google Scholar 

  23. Li, Y., Zhu, J.: A scale adaptive kernel correlation filter tracker with feature integration. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014. LNCS, vol. 8926, pp. 254–265. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16181-5_18

    Chapter  Google Scholar 

  24. Lukezic, A., Vojir, T., Cehovin, L., Matas, J., Kristan, M.: Discriminative correlation filter with channel and spatial reliability. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. IEEE (2017)

    Google Scholar 

  25. Ma, C., Huang, J.B., Yang, X., Yang, M.H.: Hierarchical convolutional features for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3074–3082. IEEE (2015)

    Google Scholar 

  26. Rifkin, R., Yeo, G., Poggio, T.: Regularized least-squares classification. Nato Sci. Ser. Sub Ser. III Comput. Syst. Sci. 190, 131–154 (2003)

    Google Scholar 

  27. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: CoRR (2014)

    Google Scholar 

  28. Sui, Y., Tang, Y., Zhang, L.: Discriminative low-rank tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3002–3010. IEEE (2015)

    Google Scholar 

  29. Sun, C., Wang, D., Lu, H., Yang, M.H.: Correlation tracking via joint discrimination and reliability learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 489–497. IEEE (2018)

    Google Scholar 

  30. Tang, M., Feng, J.: Multi-kernel correlation filter for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3038–3046. IEEE (2015)

    Google Scholar 

  31. Tang, M., Yu, B., Zhang, F., Wang, J.: High-speed tracking with multi-kernel correlation filters. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4874–4883. IEEE (2018)

    Google Scholar 

  32. Valmadre, J., Bertinetto, L., Henriques, J., Vedaldi, A., Torr, P.H.: End-to-end representation learning for correlation filter based tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 5000–5008. IEEE (2017)

    Google Scholar 

  33. Van De Weijer, J., Schmid, C., Verbeek, J., Larlus, D.: Learning color names for real-world applications. IEEE Trans. Image Process. 18(7), 1512–1523 (2009)

    Article  MathSciNet  Google Scholar 

  34. 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. IEEE (2013)

    Google Scholar 

  35. Xu, Y., Yin, W.: A block coordinate descent method for regularized multiconvex optimization with applications to nonnegative tensor factorization and completion. SIAM J. Imaging Sci. 6(3), 1758–1789 (2013)

    Article  MathSciNet  Google Scholar 

  36. Zhong, W., Lu, H., Yang, M.H.: Robust object tracking via sparsity-based collaborative model. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1838–1845. IEEE (2012)

    Google Scholar 

Download references

Acknowledgment

This work is jointly supported by National Natural Science Foundation of China (61702002, 61472002), China Postdoctoral Science Foundation, Natural Science Foundation of Anhui Province (1808085QF187), Natural Science Foundation of Anhui Higher Education Institution of China (KJ2017A017), and Co-Innovation Center for Information Supply & Assurance Technology, Anhui University.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Anhui University, Hefei, China

    Xinyan Liang, Xiao Wang, Jin Tang & Chenglong Li

  2. Center for Research on Intelligent Perception and Computing, NLPR, CASIA, Beijing, China

    Chenglong Li

Authors
  1. Xinyan Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jin Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chenglong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chenglong Li .

Editor information

Editors and Affiliations

  1. School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China

    Zongben Xu

  2. Xidian University, Xi'an, China

    Xinbo Gao

  3. Xidian University, Xi'an, Shaanxi, China

    Qiguang Miao

  4. Chinese Academy of Sciences, Beijing, China

    Yunquan Zhang

  5. Zhejiang University, Hangzhou, China

    Jiajun Bu

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Liang, X., Wang, X., Tang, J., Li, C. (2018). Learning Noise-Aware Correlation Filter for Visual Tracking. In: Xu, Z., Gao, X., Miao, Q., Zhang, Y., Bu, J. (eds) Big Data. Big Data 2018. Communications in Computer and Information Science, vol 945. Springer, Singapore. https://doi.org/10.1007/978-981-13-2922-7_14

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-2922-7_14

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2921-0

  • Online ISBN: 978-981-13-2922-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships