Skip to main content
SpringerLink
Log in

Correlation filter tracking algorithm based on spatial-temporal regularization and context awareness

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

The tracker based on correlation filters can achieve effective positioning at a relatively fast speed, resulting from its operation in the frequency domain. As a result, it is commonly employed in the field of object tracking. However, this characteristic introduces boundary effect and affects the tracking performance in some scenes. In this work, a correlation filter tracking algorithm with spatial-temporal regularization and context awareness (STCACF) is proposed: (1) the spatial-temporal information and context awareness is added to the training process to mitigate the boundary effect and enhance the overall tracking performance; (2) the tracker model adopts the iterative method of alternating direction method of multipliers (ADMM), so that each subproblem can be solved in a closed-loop solution, which can realize real-time tracking; (3) the spatial regularization is employed to reduce the influence of filter degradation. Experiments on the OTB-2013, the OTB-2015 and the TC-128 benchmark datasets demonstrate that the suggested STCACF is capable of significantly improving the tracking performance compared with state-of-the-art trackers. The STCACF tracker runs at a frame rate of approximately 22 frames per second (FPS) on a single central processing unit (CPU).

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

Similar content being viewed by others

References

  1. Wei B, Chen H, Ding Q, Luo H (2022) SiamOAN: Siamese object-aware network for real-time target tracking. Neurocomputing 471:161–174

    Article  Google Scholar 

  2. Kumar A, Walia GS, Sharma K (2020) A novel approach for multi-cue feature fusion for robust object tracking. Appl Intell 50:3201–3218

    Article  Google Scholar 

  3. Nawaratne R, Kahawala S, Nguyen S, Silva D (2021) A generative latent space approach for real-time road surveillance in smart cities. IEEE Trans Industr Inform 17(7):4872–4881

    Article  Google Scholar 

  4. Li T, Wu P, Ding F, Yang W (2020) Parallel dual networks for visual object tracking. Appl Intell 50:4631–4646

    Article  Google Scholar 

  5. Li G, Zhang S, Liu L, Zhang X, Yin Y (2021) Trajectory tracking control in real-time of dual-motor-driven driverless racing car based on optimal control theory and fuzzy logic method. Complex 2021:1–16

    Google Scholar 

  6. Cui Z, Lu N (2021) Feature selection accelerated convolutional neural networks for visual tracking. Appl Intell 51:8230–8244

    Article  Google Scholar 

  7. Mondal A (2021) Occluded object tracking using object-background prototypes and particle filter. Appl Intell 51:5259–5279

    Article  Google Scholar 

  8. Cheng Z, Savarimuthu TR (2021) A novel robot-assisted electrical impedance scanning system for subsurface object detection. Meas Sci Technol 32(8):1361–1365

    Article  Google Scholar 

  9. Chen P, Lu W (2021) Deep reinforcement learning based moving object grasping. Inf Sci 565:62–76

    Article  MathSciNet  Google Scholar 

  10. Bolme DS, Beveridge JR, Draper BA, Lui YM (2010) Visual object tracking using adaptive correlation filters. In: The twenty-third IEEE conference on computer vision and pattern recognition, San Francisco, CA, USA, pp 2544–2550

  11. Henriques J, Caseiro FR, Martins P, Batista J (2012) Exploiting the circulant structure of tracking-by-detection with kernels. In: Proceedings of the 12th European conference on computer vision - Volume Part IV, Springer, Berlin, Heidelberg, pp 702–715

  12. Abbass MY, Kwon KC, Kim N, Abdelwahab SA, FEA EI-S, Khalaf AA (2021) A survey on online learning for visual tracking. Vis Comput 37(5):993–1014

    Article  Google Scholar 

  13. Zhan Y, Zhao WL (2021) Instance search via instance level segmentation and feature representation. J Vis Commun Image R 79(1):103253

    Article  Google Scholar 

  14. Xu J, Wu X, Cao B (2021) Scale-adaptive kernel correlation filter with maximum posterior probability estimation and combined features for visual target tracking. IEEJ Trans Electr Electr 16:1076–1085

    Article  Google Scholar 

  15. Yang K, Wu X, Zhu Z, Xu J, Wan Z, Chang Y, Du Z (2020) A high-confidence model updating correlation filtering tracker with scale adaptation for visual target tracking. Optik 202:163730

    Article  Google Scholar 

  16. Liu R, Chen Q, Yao Y, Fan X, Luo Z (2021) Location-aware and regularization-adaptive correlation filters for robust visual tracking. IEEE Trans Neural Netw Learn Syst 32(6):2430–2442

    Article  Google Scholar 

  17. Liang Y, Liu Y, Yan Y, Zhang L, Wang H (2021) Robust visual tracking via spatio-temporal adaptive and channel selective correlation filters. Pattern Recogn 112(1):728–738

    Google Scholar 

  18. Elayaperumal D, Joo YH (2021) Aberrance suppressed spatio-temporal correlation filters for visual object tracking. Pattern Recogn 115:107922–107928

    Article  Google Scholar 

  19. Feng S, Shea QTK, Ng KY, Tang CN, Kwong E, Zheng Y (2021) Automatic hyoid bone tracking in real-time ultrasound swallowing videos using deep learning based and correlation filter based trackers. Sensors 21(11):3712–3719

    Article  Google Scholar 

  20. Danelljan M, Häger G, Khan FS, Felsberg M (2015) Learning spatially regularized correlation filters for visual tracking. In: International conference of IEEE on computer vision, pp 4310–4318

  21. Crammer K, Dekel O, Keshet J, Shalev-Shwartz S, Singer Y (2006) Online passive-aggressive algorithms. J Mach Learn Res 7(3):551–585

    MathSciNet  MATH  Google Scholar 

  22. Mueller M, Smith N, Ghanem B (2017) Context-aware correlation filter tracking. In: IEEE conference on computer vision and pattern recognition, pp 1387–1395

  23. Gao L, Liu B, Fu P, Xu M, Li J (2021) Visual tracking via dynamic saliency discriminative correlation filter. Appl Intell. https://doi.org/10.1007/s10489-021-02260-2

  24. Ruan W, Ye M, Wu Y, Liu W, Chen J, Liang C, Li G, Lin CW (2021) Ticnet: a target-insight correlation network for object tracking. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2021.3070677

  25. Zhang Y, Wang T, Liu K, Zhang B, Chen L (2021) Recent advances of single-object tracking methods: a brief survey. Neurocomputing 455:1–11

    Article  Google Scholar 

  26. Chen D, Tang F, Dong W, Yao H, Xu C (2021) Siamcpn: visual tracking with the Siamese center-prediction network. Computational Visual Media 7(2):253–265

    Article  Google Scholar 

  27. Wu Y, Lim J, Yang M (2013) Online object tracking: a benchmark. In: IEEE conference on computer vision and pattern recognition, pp 2411–2418

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  30. Boyd S (2010) Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends Mach Learn 3(1):1–122

    Article  MathSciNet  MATH  Google Scholar 

  31. Danelljan M, Bhat G, Khan FS, Felsberg M (2017) ECO: efficient convolution operators for tracking. In: IEEE conference on computer vision and pattern recognition, pp 6931–6939

  32. Xu T, Feng ZH, Wu XJ, Kittler J (2019) Learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking. IEEE Trans Image Process 28(11):5596–5609

    Article  MathSciNet  MATH  Google Scholar 

  33. Huang Z, Fu C, Li Y, Lin F, Lu P (2019) Learning aberrance repressed correlation filters for real-time UAV tracking. In: IEEE/CVF international conference on computer vision, pp 2891–2900

  34. Galoogahi HK, Fagg A, Lucey S (2017) Learning background-aware correlation filters for visual tracking. In: IEEE international conference on computer vision, pp 1144–1152

  35. Li Y, Fu C, Ding F, Huang Z, Lu G (2020) AutoTrack: towards high-performance visual tracking for UAV with automatic spatio-temporal regularization. In: IEEE/CVF conference on computer vision and pattern recognition, pp 11920–11929

  36. Bertinetto L, Valmadre J, Golodetz S, Miksik O, Torr PH (2016) Staple: Complementary learners for real-time tracking. In: IEEE conference on computer vision and pattern recognition, pp 1401–1409

  37. Danelljan M, Häger G, Khan FS, Felsberg M (2017) Discriminative scale space tracking. IEEE Trans Pattern Anal Mach Intell 39:1561–1575

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant (61671222, 61903162), the Postgraduate Research & Practice Innovation Program of Jiangsu Province under Grant (KYCX21_3481, KYCX21_3484).

Author information

Authors and Affiliations

  1. School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang, 212100, China

    Xuedong Wu, Jie Xu, Zhiyu Zhu, Qiang Zhang, Siming Tang, Mengquan Liang & Baiheng Cao

  2. College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China

    Yaonan Wang

Authors
  1. Xuedong Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jie Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhiyu Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yaonan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qiang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Siming Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Mengquan Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Baiheng Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xuedong Wu.

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

Wu, X., Xu, J., Zhu, Z. et al. Correlation filter tracking algorithm based on spatial-temporal regularization and context awareness. Appl Intell 52, 17772–17783 (2022). https://doi.org/10.1007/s10489-022-03458-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-022-03458-8

Keywords

Search

Navigation