Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Object Tracking with Multi-sample Correlation Filters

  • Conference paper
  • First Online:
Neural Information Processing (ICONIP 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12532))

Included in the following conference series:

  • 2392 Accesses

Abstract

Due to the real-time tracking and location accuracy of the kernel correlation filtering (KCF) algorithm, this method is now widely used in object tracking tasks. However, whether KCF or its improved algorithm, the filter parameter training is achieved by the feature of the current frame, in other words, the training sample is single. If the samples of multiple frames in the previous sequence can be integrated to the filter parameters training, the trained filter parameters should be more reliable. In this paper, we propose an object tracking algorithm based on multi-sample kernel correlation filtering (MSKCF). Meanwhile, In order to select better samples, the average peak correlation energy (APCE) is introduced to measure the stability of tracking effect, and is applied as weight of sample. The frames with higher APCE value are chosen as multi-sample, and then are used to train filter parameters. Experimental results show that the tracking effect of the proposed method is superior to compared state-of-the-art algorithms.

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
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Mei, X., Ling, H.: Robust visual tracking using l1 minimization. In: Proceedings of ICCV, pp. 1436–1443. IEEE, Kyoto (2009)

    Google Scholar 

  2. Wang, D., Lu, H., Yang, M.: Least soft-threshold squares tracking. In: Proceedings of CVPR, pp. 2371–2378. IEEE, Portland (2013)

    Google Scholar 

  3. Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: Proceedings of CVPR, pp. 142–149, IEEE, Hilton Head Island (2000)

    Google Scholar 

  4. Achanta, R., Susstrunk, S.: Saliency detection using maximum symmetric surround. In: Proceedings of ICIP, pp. 2653–2656. IEEE, Hong Kong (2010)

    Google Scholar 

  5. Babenko, B., Yang, M., Belongie, S.: Visual tracking with online multiple instance learning. In: Proceedings of CVPR, pp. 983–990. IEEE, Miami (2009)

    Google Scholar 

  6. Hare, S., Golodetz, S., Saffari, A., et al.: Struck: structured output tracking with kernels. IEEE Trans. Pattern Anal. Mach. Intell. 38(10), 2096–2109 (2016)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  9. Bolme, D.S., Beveridge, J.R., Draper, B.A., Lui, Y.M.: Visual object tracking using adaptive correlation filters. In: Proceedings of CVPR, pp. 2544–2550. IEEE, San Francisco (2010)

    Google Scholar 

  10. 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 

  11. Martin, D., Danelljan, G.H., Fahad, S.K., Michael, F.: Discriminative scale space tracking. IEEE Trans. Pattern Anal. Mach. Intell. 39(8), 1561–1575 (2017)

    Article  Google Scholar 

  12. Martin, D., Gustav, H., Fahad, S. K., Michael, F.: Learning spatially regularized correlation filters for visual tracking. In: Proceedings of ICCV, pp. 4310–4318. IEEE, Santiago (2015)

    Google Scholar 

  13. Wang, M., Liu, Y., Huang, Z.: Large margin object tracking with circulant feature maps. In: Proceedings of CVPR, pp. 4800–4808. IEEE, Honolulu (2017)

    Google Scholar 

  14. Wang, N., Yeung, D. Y.: Learning a deep compact image representation for visual tracking. In: Proceedings of NIPS, Nevada, USA, pp. 809–817 (2013)

    Google Scholar 

  15. Wang, L., Ouyang, W., Wang, X., Lu, H.: Visual tracking with fully convolutional networks. In: Proceedings of ICCV, pp. 3119–3127. IEEE, Santiago (2015)

    Google Scholar 

  16. 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 

  17. Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., Torr, P.H.S.: Fully-convolutional siamese networks for object tracking. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 850–865. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48881-3_56

    Chapter  Google Scholar 

  18. Valmadre, J., Bertinetto, L., Henriques, J., Vedaldi, A., Torr, P. H.: End-to-end representation learning for correlation filter based tracking. In: Proceedings of CVPR, pp. 5000–5008. IEEE, Honolulu (2017)

    Google Scholar 

  19. Li, B., Wu, W., Zhu, Z., Hu, X.: High performance visual tracking with Siamese region proposal network. In: Proceedings of CVPR, pp. 8971–8980. IEEE, Salt Lake City (2018)

    Google Scholar 

  20. Wang, Q., Zhang, L., Bertinetto, L., Hu, W., Torr, P. H.: Fast online object tracking and segmentation: a unifying approach. In: Proceedings of CVPR, pp. 1328–1338 (2019)

    Google Scholar 

  21. Tang, M., Feng, J.: Multi-kernel correlation filter for visual tracking. In: Proceedings of ICCV, pp. 3038–3046. IEEE, Santiago (2015)

    Google Scholar 

  22. Li, F., Tian, C., Zuo, W., Zhang, L., Yang, M.: Learning spatial-temporal regularized correlation filters for visual tracking. In: Proceedings of CVPR, pp. 4904–4913. IEEE, Salt Lake City (2018)

    Google Scholar 

  23. Tang, M., Yu, B., Zhang, F., Wang, J.: High-speed tracking with multi-kernel correlation filters. In: Proceedings of CVPR, pp. 4874–4883. IEEE, Salt Lake City (2018)

    Google Scholar 

  24. Bibi, A., Ghanem, B.: Multi-template scale-adaptive kernelized correlation filters. In: Proceedings of ICCV, pp. 613–620. IEEE, Santiago (2015)

    Google Scholar 

  25. Wang, N., Zhow, W., Tian, Q., Hong, R., Wang, M., Li, H.: Multi-cue correlation filters for robust visual tracking. In: Proceedings of CVPR, pp. 4844–4853. IEEE, Salt Lake City (2018)

    Google Scholar 

  26. Scholkopf, B., Smola, A.J.: Learning with kernels - support vector machines, regularization, optimization, and beyond. MIT Press, Cambridge (2001)

    Google Scholar 

  27. Ma, C., Huang, J., Yang, X., Yang, M.: Hierarchical convolutional features for visual tracking. In: Proceedings of CVPR, pp. 3074–3082. IEEE, Santiago (2015)

    Google Scholar 

  28. Nam, H., Han, B.: Learning multi-domain convolutional neural networks for visual tracking. In: Proceedings of CVPR, pp. 4293–4302. IEEE, Las Vegas (2016)

    Google Scholar 

  29. Ma, Y., Yuan, C., Gao, P., Wang, F.: Efficient multi-level correlating for visual tracking. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11365, pp. 452–465. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20873-8_29

    Chapter  Google Scholar 

  30. Wu, Y., Lim, J., Yang, J.: Object tracking benchmark. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1834–1848 (2015)

    Article  Google Scholar 

  31. Kristan, M., et al.: The visual object tracking VOT2016 challenge results. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 777–823. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48881-3_54

    Chapter  Google Scholar 

  32. 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 

  33. Danelljan, M., Hhger, G., Khan, F.S., Felsberg, M.: Discriminative scale space tracking. IEEE Trans. Pattern Anal. Mach. Intell. 39(8), 1561–1575 (2017)

    Article  Google Scholar 

  34. Danelljan, M., Bhat, G., Khan, F. S., Felsberg, M.: ECO: Efficient convolution operators for tracking. In: Proceedings of CVPR, pp. 6931–6939. IEEE, Honolulu (2017)

    Google Scholar 

  35. 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 

Download references

Author information

Authors and Affiliations

  1. Samuel Ginn College of Engineering, Auburn University, Auburn, AL, 36849, USA

    Qinyi Tian

Authors
  1. Qinyi Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qinyi Tian .

Editor information

Editors and Affiliations

  1. Department of AI, Ping An Life, Shenzhen, China

    Haiqin Yang

  2. Faculty of Information Technology, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

    Kitsuchart Pasupa

  3. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi-Sing Leung

  4. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong

    James T. Kwok

  5. School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Jonathan H. Chan

  6. The Chinese University of Hong Kong, New Territories, Hong Kong

    Irwin King

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tian, Q. (2020). Object Tracking with Multi-sample Correlation Filters. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science(), vol 12532. Springer, Cham. https://doi.org/10.1007/978-3-030-63830-6_40

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-63830-6_40

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63829-0

  • Online ISBN: 978-3-030-63830-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation