Skip to main content
SpringerLink
Log in

Distractor-aware visual tracking using hierarchical correlation filters adaptive selection

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

In recent years, the ensembled trackers composed of multi-level features from the pre-trained Convolutional Neural Network (CNN) have achieved top performance in visual tracking. However, due to the background clutters and the distractors in the search area, the tracker tends to drift towards an area that is similar to the target. In order to suppress interference of background and similar objects, we propose an effective Distractor-Aware Map (DAM), which can reduce the weights of the interference area in the multi-level features. Thus, the tracker can focus on the target to greatly eliminate the risk of drift. In addition, we build a Hierarchical Correlation Filters Model (HCFM) based on the multi-level convolutional features to track targets in parallel. To further improve the robustness of tracking, a novel Multi-Model Adaptive Selection (MAS) mechanism is presented. This mechanism can evaluate the confidence of the response map in HCFM to adaptively select the most reliable model. Finally, in order to appropriately update the model to adapt to appearance changes of the target, we propose an adaptive updating strategy for the updates of the DAM and HCFM. We perform comprehensive experiments on OTB-2013, OTB-2015 and Temple Color datasets and the experimental results show the superiority of our algorithm over other state-of-the-art approaches.

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
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Mei X, Ling H (2011) Robust visual tracking and vehicle classification via sparse representation. IEEE Trans Pattern Anal Mach Intell 33(11):2259–2272

    Article  Google Scholar 

  2. Adam A, Rivlin E, Shimshoni I (2006) Robust fragments-based tracking using the integral histogram. In: 2006 IEEE Computer society conference on computer vision and pattern recognition (CVPR’06), vol 1. IEEE, pp 798–805

  3. Kalal Z, Mikolajczyk K, Matas J (2011) Tracking-learning-detection. IEEE Trans Pattern Anal Mach Intell 34(7):1409–1422

    Article  Google Scholar 

  4. Hong S, You T, Kwak S, Han B (2015) Online tracking by learning discriminative saliency map with convolutional neural network. In: International conference on machine learning, pp 597–606

  5. Dai K, Wang D, Lu H, Sun C, Li J (2019) Visual tracking via adaptive spatially-regularized correlation filters. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4670–4679

  6. Huang Z, Fu C, Li Y, Lin F, Lu P (2019) Learning aberrance repressed correlation filters for real-time uav tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp 2891–2900

  7. Xu T, Feng Z-H, Wu X-J, Kittler J (2019) Joint group feature selection and discriminative filter learning for robust visual object tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp 7950–7960

  8. Zhang J, Xie Z, Sun J, Zou X, Wang J (2020) A cascaded r-cnn with multiscale attention and imbalanced samples for traffic sign detection. IEEE Access 8:29742–29754

    Article  Google Scholar 

  9. Tu Y, Lin Y, Wang J (2018) Semi-supervised learning with generative adversarial networks on digital signal mod-ulation classification. Comput Mater Cont 55(2):243–254

    Google Scholar 

  10. Ma C, Huang J-B, Yang X, Yang M-H (2015) Hierarchical convolutional features for visual tracking. In: Proceedings of the IEEE international conference on computer vision, pp 3074–3082

  11. Zhang J, Jin X, Sun J, Wang J, Sangaiah A K (2020) Spatial and semantic convolutional features for robust visual object tracking. Multimed Tools Appl 79(21):15095–15115

    Article  Google Scholar 

  12. Zhang J, Wu Y, Feng W, Wang J (2019) Spatially attentive visual tracking using multi-model adaptive response fusion. IEEE Access 7:83873–83887

    Article  Google Scholar 

  13. He Z, Fan Y, Zhuang J, Dong Y, Bai H (2017) Correlation filters with weighted convolution responses. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp 1992–2000

  14. Song Y, Ma C, Gong L, Zhang J, Lau Rynson WH, Yang M-H (2017) Crest: Convolutional residual learning for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp 2555–2564

  15. Liu J, Zhang B, Cheng X, Chen Y, Zhao L (2019) An adaptive superpixel tracker using multiple features. CMC-Comput Mater Cont 60(3):1097–1108

    Google Scholar 

  16. Danelljan M, Hager G, Shahbaz Khan F, Felsberg M (2015) Learning spatially regularized correlation filters for visual tracking. In: Proceedings of the IEEE international conference on computer vision, pp 4310–4318

  17. Qi Y, Zhang S, Qin L, Yao H, Huang Q, Lim J, Yang M-H (2016) Hedged deep tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4303–4311

  18. Santhosh PK, Kaarthick B (2019) An automated player detection and tracking in basketball game. CMC-Comput. Mater. Contin 58:625–639

    Article  Google Scholar 

  19. Wu Y, Lim J, Yang M-H (2013) Online object tracking: A benchmark. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2411–2418

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

    Article  Google Scholar 

  21. Liang P, Blasch E, Ling H (2015) Encoding color information for visual tracking: Algorithms and benchmark. IEEE Trans Image Process 24(12):5630–5644

    Article  MathSciNet  Google Scholar 

  22. Bolme D S, Beveridge J R, Draper B A, Lui Y M (2010) Visual object tracking using adaptive correlation filters. In: 2010 IEEE computer society conference on computer vision and pattern recognition. IEEE, pp 2544–2550

  23. Henriques J F, Caseiro R, Martins P, Batista J (2014) High-speed tracking with kernelized correlation filters. IEEE Trans Pattern Ana Mach Intell 37(3):583–596

    Article  Google Scholar 

  24. Henriques J F, Caseiro R, Martins P, Batista J (2012) Exploiting the circulant structure of tracking-by-detection with kernels. In: European conference on computer vision. Springer, pp 702–715

  25. Danelljan M, Robinson A, Khan F S, Felsberg M (2016) Beyond correlation filters: Learning continuous convolution operators for visual tracking. In: European conference on computer vision. Springer, pp 472–488

  26. Danelljan M, Bhat G, Shahbaz Khan F, Felsberg M (2017) Eco: Efficient convolution operators for tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6638–6646

  27. Li Y, Zhu J (2014) A scale adaptive kernel correlation filter tracker with feature integration. In: European conference on computer vision. Springer, pp 254–265

  28. Danelljan M, Häger G, Khan F, Felsberg M (2014) Accurate scale estimation for robust visual tracking. In: British Machine Vision Conference. BMVA Press, Nottingham

  29. Lukezic A, Vojir T, Cehovin Zajc L, Matas J, Kristan M (2017) Discriminative correlation filter with channel and spatial reliability. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 6309–6318

  30. Du W, Wang Y, Qiao Y (2017) Rpan: An end-to-end recurrent pose-attention network for action recognition in videos. In: Proceedings of the IEEE International Conference on Computer Vision, pp 3725–3734

  31. Wang F, Jiang M, Qian C, Yang S, Li C, Zhang H, Wang X, Tang X (2017) Residual attention network for image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3156–3164

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

  33. Doi M, Matsumoto T, Kimachi A, Nishi S, Ikoma N (2014) Robust color object tracking method against illumination color change. In: 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS). IEEE, pp 718–722

  34. Nummiaro K, Koller-Meier E, Van Gool L (2003) An adaptive color-based particle filter. Image Vis Comput 21(1):99–110

    Article  Google Scholar 

  35. Zhu G, Wang J, Wu Y, Zhang X, Lu H (2016) Mc-hog correlation tracking with saliency proposal. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 3690–3696

  36. Zhang K, Liu Q, Yang J, Yang M-H (2018) Visual tracking via boolean map representations. Pattern Recogn 81:147–160

    Article  Google Scholar 

  37. Zhu Z, Wu W, Zou W, Yan J (2018) End-to-end flow correlation tracking with spatial-temporal attention. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 548–557

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

  39. Lee D-Y, Sim J-Y, Kim C-S (2015) Multihypothesis trajectory analysis for robust visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 5088–5096

  40. Zhang J, Ma S, Sclaroff S (2014) Meem: robust tracking via multiple experts using entropy minimization. In: European conference on computer vision. Springer, pp 188–203

  41. Ma C, Yang X, Zhang C, Yang M-H (2015) Long-term correlation tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5388–5396

  42. Wang N, Zhou W, Tian Q, Hong R, Wang M, Li H (2018) Multi-cue correlation filters for robust visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4844–4853

  43. Fan H, Ling H (2017) Parallel tracking and verifying: A framework for real-time and high accuracy visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp 5486–5494

  44. Gao Y, Ji R, Zhang L, Hauptmann A (2014) Symbiotic tracker ensemble toward a unified tracking framework. IEEE Trans Circ Syst Video Technol 24(7):1122–1131

    Article  Google Scholar 

  45. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556

  46. Possegger H, Mauthner T, Bischof H (2015) In defense of color-based model-free tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2113–2120

  47. Rezatofighi H, Tsoi N, Gwak J, Sadeghian A, Reid I, Savarese S (2019) Generalized intersection over union: A metric and a loss for bounding box regression. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 658–666

  48. Bertinetto L, Valmadre J, Henriques J F, Vedaldi A, Torr Philip HS (2016) Fully-convolutional siamese networks for object tracking. In: European conference on computer vision. Springer, pp 850–865

  49. Danelljan M, Hager G, Shahbaz Khan F, Felsberg M (2015) Convolutional features for correlation filter based visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp 58–66

  50. Valmadre J, Bertinetto L, Henriques J, Vedaldi A, Torr PHS (2017) End-to-end representation learning for correlation filter based tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2805–2813

  51. Bibi A, Mueller M, Ghanem B (2016) Target response adaptation for correlation filter tracking. In: European conference on computer vision. Springer, pp 419–433

  52. Hong Z, Chen Z, Wang C, Mei X, Prokhorov D, Tao D (2015) Multi-store tracker (muster): A cognitive psychology inspired approach to object tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 749–758

  53. Zhang T, Xu C, Yang M-H (2017) Multi-task correlation particle filter for robust object tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4335–4343

  54. Li F, Tian C, Zuo W, Zhang L, Yang M-H (2018) Learning spatial-temporal regularized correlation filters for visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4904–4913

  55. Kiani Galoogahi H, Fagg A, Lucey S (2017) Learning background-aware correlation filters for visual tracking. In: Proceedings of the IEEE international conference on computer vision, pp 1135–1143

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61972056, in part by the Basic Research Fund of Zhongye Changtian International Engineering Co., Ltd. under Grant 2020JCYJ07, in part by the Research Fund of Changsha New Smart City Research Association under Grant 2020YB006, in part by the “Double First-class” International Cooperation and Development Scientific Research Project of Changsha University of Science and Technology under Grant 2019IC34, and in part by the Postgraduate Training Innovation Base Construction Project of Hunan Province under Grant 2019-248-51.

Author information

Authors and Affiliations

  1. Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China

    Jianming Zhang, Yang Liu, Hehua Liu & Jin Wang

  2. Department of Informatics, University of Leicester, Leicester, LE1 7RH, UK

    Yudong Zhang

Authors
  1. Jianming Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hehua Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yudong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianming Zhang.

Ethics declarations

Conflict of Interests

The authors declare no conflict of interest.

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

Zhang, J., Liu, Y., Liu, H. et al. Distractor-aware visual tracking using hierarchical correlation filters adaptive selection. Appl Intell 52, 6129–6147 (2022). https://doi.org/10.1007/s10489-021-02694-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-021-02694-8

Keywords

Search

Navigation