Skip to main content
SpringerLink
Log in

Long-Term Tracking Algorithm Based on Kernelized Correlation Filter

  • Conference paper
  • First Online:
Proceedings of the Fifth Euro-China Conference on Intelligent Data Analysis and Applications (ECC 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 891))

  • 693 Accesses

Abstract

KCF (Kernelized Correlation Filter) is a classical tracking algorithm based on correlation filter, which has good performance in short-term tracking. But when the object is partially or fully occluded, or disappeared in the view, KCF doesn’t work well. In this paper, a long-term tracking algorithm based on KCF is proposed. HOG (Histogram of Oriented Gradient) and LAB three-channel color information are employed to represent the object, and a re-detection module is added into the KCF tracking procedure. The peak ratio is introduced to control the start of the re-detection module, and a correlation filter model based on SURF feature points is re-learned to continuously track the occluded object. Experimental results on OTB dataset show that our algorithm has higher tracking accuracy than other five trackers, and is suitable for long-term tracking.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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

References

  1. Wang, Y.X., Zhao, Q.J., Zhao, L.J.: Robust object tracking based on FREAK and P3CA. Chin. J. Comput. 38, 1188–1201 (2016). https://doi.org/10.11897/SP.J.1016.2015.01188

    Article  MathSciNet  Google Scholar 

  2. Wang, Y.X., Zhao, Q.J., Cai, Y.M., et al.: Tracking by auto-reconstruction particle filter trackers. Chin. J. Comput. 39, 1294–1306 (2016). https://doi.org/10.11897/SP.J.1016.2016.01294

    Article  Google Scholar 

  3. Jiang, W.T., Liu, W.J., Yuan, H., et al.: Research of object tracking based on soft feature theory. Chin. J. Comput. 39, 1334–1355 (2016). https://doi.org/10.11897/SP.J.1016.2016.01334

    Article  MathSciNet  Google Scholar 

  4. Bolme, D.S., Beveridge, J.R., Draper, B.A.: Visual object tracking using adaptive correlation filters. In: Computer Vision and Pattern Recognition, pp. 2544–2550. IEEE (2010). https://doi.org/10.1109/cvpr.2010.5539960

  5. Henriques, J.F., Rui, C., Martins, P., et al.: Exploiting the Circulant Structure of Tracking-by-Detection with Kernels. Lecture Notes in Computer Science, vol. 7575, 702–715 (2012). https://doi.org/10.1007/978-3-642-33765-9_50

    Chapter  Google Scholar 

  6. Henriques, J.F., Caseiro, R., Martins, P., et al.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37, 583–596 (2015). https://doi.org/10.1109/TPAMI.2014.2345390

    Article  Google Scholar 

  7. Li, Y., Zhu, J.A.: A scale adaptive kernel correlation filter tracker with feature integration. In: European Conference on Computer Vision, pp. 254–265. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-16181-5_18

    Google Scholar 

  8. Danelljan, M., Häger, G., Fahad, S.K., et al.: Accurate scale estimation for robust visual tracking. In: British Machine Vision Conference, pp. 1–5. BMVA Press (2014). https://doi.org/10.5244/c.28.65

  9. Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. IEEE Trans. Pattern Anal. Mach. Intell. 34, 1409–1422 (2012). https://doi.org/10.1109/TPAMI.2011.239

    Article  Google Scholar 

  10. Zhang, K., Zhang, L., Yang, M.H., et al.: Fast visual tracking via dense spatio-temporal context learning. In: European Conference on Computer Vision, pp. 127–141. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_9

    Google Scholar 

  11. Xu, J.Q., Lu, Y.: Robust visual tracking via weighted spatio-temporal learning. Acta Automatica Sinica 41, 1901–1912 (2015). https://doi.org/10.16383/j.aas.2015.c150073

    Article  Google Scholar 

  12. Liu, W., Zhao, W.J., Li, C.: Long-term visual tracking based on spatio-temporal context. Acta Optica Sinica 36, 179–186 (2016). https://doi.org/10.3788/AOS201636.0115001

    Article  Google Scholar 

  13. Ma, C., Yang, X., Zhang, C., et al.: Long-term correlation tracking. In: Computer Vision and Pattern Recognition, pp. 5388–5396. IEEE (2015). https://doi.org/10.1109/cvpr.2015.7299177

  14. Zhao, G.P., Shen, Y.P., Wang. J.Y.: Adaptive feature fusion object tracking based on circulant structure with kernel. Acta Optica Sinica 37, 0815001. https://doi.org/10.3788/aos201737

  15. Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2411–2418. IEEE (2013). https://doi.org/10.1109/cvpr.2013.312

  16. Danelljan, M., Khan, F.S., Felsberg, M., et al.: Adaptive color attributes for real-time visual tracking. In: Computer Vision and Pattern Recognition, pp. 1090–1097. IEEE (2014). https://doi.org/10.1109/cvpr.2014.143

Download references

Acknowledgments

The work is sponsored by the Shaanxi International Cooperation Exchange Funded Projects (2017KW-013, 2017KW-016), Graduate Creative Funds of Xi’an University of Posts and Telecommunications (CXJJ2017004).

Author information

Authors and Affiliations

  1. Center for Image and Information Processing, Xi’an University of Posts and Telecommunications, Xi’an, 710121, China

    Na Li, Lingfeng Wu & Daxiang Li

  2. Key Laboratory of Electronic Information Application Technology for Scene Investigation, Ministry of Public Security, Xi’an, 710121, China

    Na Li, Lingfeng Wu & Daxiang Li

  3. International Joint Research Center for Wireless Communication and Information Processing, Xi’an, 710121, China

    Na Li, Lingfeng Wu & Daxiang Li

Authors
  1. Na Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lingfeng Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daxiang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Na Li or Lingfeng Wu .

Editor information

Editors and Affiliations

  1. Department of Computer Science, VSB-Technical University of Ostrava, Ostrava, Czech Republic

    Pavel Krömer

  2. School of Automation, Xi’an University of Posts and Telecommunications, Xi’an, China

    Hong Zhang

  3. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China

    Yongquan Liang

  4. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Jeng-Shyang Pan

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Li, N., Wu, L., Li, D. (2019). Long-Term Tracking Algorithm Based on Kernelized Correlation Filter. In: Krömer, P., Zhang, H., Liang, Y., Pan, JS. (eds) Proceedings of the Fifth Euro-China Conference on Intelligent Data Analysis and Applications. ECC 2018. Advances in Intelligent Systems and Computing, vol 891. Springer, Cham. https://doi.org/10.1007/978-3-030-03766-6_84

Download citation

Publish with us

Policies and ethics

Search

Navigation