Skip to main content
SpringerLink
Log in

Dual-template adaptive correlation filter for real-time object tracking

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Visual object tracking is a hot topic in the field of computer vision. The drift of the tracking box or loss of the tracking target often occurs for existing correlation filter based trackers when the target moves quickly or deforms. Focusing on this problem, we propose a dual-template adaptive correlation filter for real-time object tracking. First, we trained templates for different size levels. Second, the best template was selected based on the target response confidence during estimation of the target translation. Third, the dual templates, scale estimation component, and feature fusion component were integrated into the benchmark tracker, the kernelized correlation filter. The object tracking benchmark was used to evaluate the performance of the proposed algorithm. The experimental results show that compared with the benchmark tracker, the average overlap precision and distance precision of this proposed algorithm are increased by 23.2% and 9.4% in OTB-100. The average running frame rate reaches 42 frames per second, which can meet the real-time requirements. At the same time, five algorithms, DSST, SAMF, KCF, CN, and CSK, appear to drift or even lose the target among the four selected typical video sequences, while our algorithm can successfully track the target.

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

Similar content being viewed by others

References

  1. Abualigah L (2020) Multi-verse optimizer algorithm: a comprehensive survey of its results, variants, and applications, Neur Comput Appl 1–21

  2. Akin O, Mikolajczyk K (2014) Online learning and detection with part-based, circulant structure. International Conference on Pattern Recognition, pp. 4230–4233

  3. Bertinetto L, Valmadre J et al. (2016) Staple: complementary learners for real-time tracking. Proc. CVPR, Las Vegas, NV, USA, pp. 1401–1409

  4. Bolme DS, Beveridge JR et al. (2010) Visual object tracking using adaptive correlation filters. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp 2544–2550

  5. Chao M, Huang JB, Yang X, Yang MH (2018) Adaptive Correlation Filters with Long-Term and Short-Term Memory for Object Tracking. Int J Compr Vis, pp. 1–26

  6. Chu J, Tu X, Leng L, Miao J (2020) Double-Channel object tracking with position deviation suppression. IEEE Access 8:856–866

    Article  Google Scholar 

  7. Danelljan M, Hager G, Khan FS, Felsberg M (2014) Accurate scale estimation for robust visual tracking. Proc. BMVA, Nottingham, U.K., pp. 702–715

  8. Danelljan M, Khan FS, Felsberg M et al. (2014) Adaptive color attributes for real-time visual tracking. IEEE Conference on Computer Vision & Pattern Recognition, pp 1090–1097

  9. Danelljan M, Gustav H, Khan F, Felsberg M (2016) Learning spatially regularized correlation filters for visual tracking. Proc IEEE Int Conf Comput Vis 2015:4310–4318

    Google Scholar 

  10. Danelljan M, Hager G, Khan FS, Felsberg M (2017) Discriminative Scale Space Tracking. IEEE Transact Patt Anal Mach Intell 39:1561–1575

    Article  Google Scholar 

  11. Dong X, Shen J (2018) Triplet loss in Siamese network for object tracking. ECCV 2018. Lect Notes Comput Sci 11217:472–488

    Article  Google Scholar 

  12. Dong X, Shen J, Yu D, Wang W, Liu J, Huang H (2017) Occlusion-aware real-time object tracking. IEEE Transact Multimed 19:763–771

    Article  Google Scholar 

  13. Dong X, Shen J, Wu D et al (2019) Quadruplet network with one-shot learning for fast visual object tracking. IEEE Trans Image Process 28:3516–3527

    Article  MathSciNet  Google Scholar 

  14. Galoogahi HK, Sim T, Lucey S (2015) Correlation Filters with Limited Boundaries. Computer Vision & Pattern Recognition, pp. 4630–4638

  15. Galoogahi HK, Fagg A, Lucey S (2017) Learning Background-Aware Correlation Filters for Visual Tracking. IEEE International Conference on Computer Vision (ICCV), 1144–1152

  16. Henriques JF, Rui C, Martins P, Batista J (2012) Exploiting the Circulant Structure of Tracking-by-Detection with Kernels. Proc Eur Conf Comput Vis 702–715

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

    Article  Google Scholar 

  18. Hu H, Ma B, Shen J, Sun H, Shao L, Porikli F (2019) Robust object tracking using manifold regularized convolutional neural networks. IEEE Transact Multimed 21:510–521

    Article  Google Scholar 

  19. Lee M, Kim T, Ban Y, Song E, Lee S (2019) Sampling operator to learn the scalable correlation filter for visual tracking. IEEE Access 7:11554–11564

    Article  Google Scholar 

  20. Leng L, Teoh ABJ, Li M, Khan MK (2014) A remote cancelable palmprint authentication protocol based on multi-directional two-dimensional PalmPhasor-fusion. Secur Commun Netw 7:1860–1871

    Article  Google Scholar 

  21. Leng L, Li M, Kim C, Bi X (2017) Dual-source discrimination power analysis for multi-instance contactless palmprint recognition. Multimed Tools Appl 76:333–354

    Article  Google Scholar 

  22. Liang Z, Shen J (2020) Local semantic Siamese networks for fast tracking. IEEE Trans Image Process 29:3351–3364

    Article  Google Scholar 

  23. Liu Q, Hu G, Islam MM (2018) Fast visual tracking with Robustifying Kernelized correlation filters. IEEE Access 6:43302–43314

    Article  Google Scholar 

  24. Ma B, Shen J, Liu Y, Hu H, Shao L, Li X (2015) Visual tracking using strong classifier and structural local sparse descriptors. IEEE Transact Multimed 17:1818–1828

    Article  Google Scholar 

  25. Ma C, Yang X, Zhang C, Yang MH (2015) Long-term correlation tracking," IEEE Conf. Comput. Vis. Pattern Recognit., pp. 5388–5396

  26. Ma B, Hu H, Shen J, Liu Y, Shao L (2016) Generalized pooling for robust object tracking. IEEE Trans Image Process 25(9):4199–4208

    MathSciNet  MATH  Google Scholar 

  27. Mueller M, Smith N, Ghanem B (2017) Context-Aware Correlation Filter Tracking. Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 1396–1404

  28. Shen J, Tang X, Dong X, Shao L (2019) Visual object tracking by hierarchical attention Siamese network. IEEE Transact Cybernet 1–13

  29. Shen J, Liang Z, Liu J, Sun H, Shao L, Tao D (2019) Multiobject tracking by submodular optimization. IEEE Transact Cybernet 49:1990–2001

    Article  Google Scholar 

  30. Sun L, Zhao C et al (2019) A novel weakly-supervised approach for RGB-D-based nuclear waste object detection. IEEE Sensors J 19:3487–3500

    Article  Google Scholar 

  31. Tan K, Wei Z (2019) Learning an orientation and scale adaptive tracker with regularized correlation filters. IEEE Access 7:53476–53486

    Article  Google Scholar 

  32. Tang Z, Yu H, Lu C, Liu P, Jin X (2019) Single-trial classification of different movements on one arm based on ERD/ERS and Corticomuscular coherence. IEEE Access 7:128185–128197

    Article  Google Scholar 

  33. Wang N, Shi J, Yeung DY, Jia J (2015) Understanding and diagnosing visual tracking systems. Proc. IEEE Int. Conf. Comput. Vis., pp. 3101-3109

  34. Wang M, Yong L, Huang Z (2017) Large Margin Object Tracking with Circulant Feature Maps. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4800–4808

  35. Wu Y, Lim J, Yang M (2013) Online object tracking: a benchmark. IEEE Conference on Computer Vision and Pattern Recognition, pp 2411–2418

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

    Article  Google Scholar 

  37. Yang L, Zhu J (2014) A scale adaptive kernel correlation filter tracker with feature integration. Proc. ECCV, Zurich, Switzerland, pp. 254–265

  38. Yuan Y, Chu J, Leng L, Miao J, Kim B (2020) A scale-adaptive object-tracking algorithm with occlusion detection. EURASIP J Image Vid Process 7:1–15

    Google Scholar 

Download references

Funding

This work was supported by Zhejiang Province Science Foundation for Public Welfare (LGG19F020014), National Natural Science Foundation of China (61671192) and China Postdoctoral Science Foundation (2017 M621796).

Author information

Authors and Affiliations

  1. School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, 310018, China

    Junrong Yan, Luchao Zhong, Yingbiao Yao, Xin Xu & Chenjie Du

Authors
  1. Junrong Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luchao Zhong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yingbiao Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xin Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chenjie Du
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yingbiao Yao.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yan, J., Zhong, L., Yao, Y. et al. Dual-template adaptive correlation filter for real-time object tracking. Multimed Tools Appl 80, 2355–2376 (2021). https://doi.org/10.1007/s11042-020-09644-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-09644-5

Keywords

Search

Navigation