Skip to main content
SpringerLink
Log in

Multi-features guided robust visual tracking

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

Abstract

This paper focuses on dealing with the tracking challenges such as target occlusion and deformation. It proposes a new tracking method via extracting and evaluating multi-features for both target region and its adjacent surroundings. The multi-features separately describe the key factors to detect target including the color feature, the shape and contour feature, and the distributions of structure and intensity described by the Pearson Correlation Coefficient. These multi-features are proposed as the basic representation of target template and candidates and used to define a matching algorithm between them. The best matched candidate is taken as the final tracking result. To improve the efficiency of target template and candidates, the region of importance (ROI) for target is proposed by evaluating the distribution of salient values on many extended regions. The ROIs produce more accurate regions to form target template and candidates. Finally, a new template update method is defined based on the precision of tracked result to adapt to target state and achieve the follow target tracking. Using 25 videos in visual tracking benchmark, we achieve the quantitative and qualitatively evaluations of 12 different trackers. Many experiments demonstrate that our tracker produces much better results than the present trackers in dealing with target occlusion, deformation, rotation, background clutters.

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. 7
Fig. 6
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Adam A, Rivlin E, Shimshoni I (2006) Robust fragments -based tracking using the integral histogram. IEEE conference on computer vision and pattern recognition, p.798-805

  2. Bibi A, Mueller M, Ghanem B (2016) Target response adaptation for correlation filter tracking. European conference on computer vision, p.419-433. 2

  3. Bolme DS, Beveridge JR, Draper BA, et al. (2010) Visual object tracking using adaptive correlation filters. IEEE conference on computer vision and pattern recognition, p.2544–2550

  4. Cai Z, Wen L, Yang J, et al. (2012) Structured visual tracking with dynamic graph. Asian Conference on Computer Vision. Springer Berlin Heidelberg, p.86–97

  5. Choi J, Chang HJ, Fischer T, Yun S, Lee K, Jeong J, Demiris Y, Choi JY (2018) Context-Aware Deep Feature Compression for High-Speed Visual Tracking. IEEE conference on computer vision and pattern recognition, p.479–488

  6. Dinh TB, Vo N, and Medioni G (2011) Context Tracker: Exploring supporters and distracters in unconstrained environments. IEEE conference on computer vision and pattern recognition, p.1177–1184

  7. Godec M, Roth PM, Bischof H (2013) Hough-based tracking of non-rigid objects. Comput Vis Image Underst 117(10):1245–1256

    Article  Google Scholar 

  8. Grabner M, Grabner H, Bischof H (2007) Learning features for tracking. IEEE conference on computer vision and pattern recognition, p.1–8

  9. Guo Y, Chen Y, Tang F, Li A, Luo W, Liu M (2014) Object tracking using Learned feature manifolds. Comput Vis Image Underst 118:128–139

    Article  Google Scholar 

  10. Hare S, Saffari A, and Torr PHS (2011) Struck: structured output tracking with kernels. International conference on computer vision, p.2096–2109

  11. Henriques JF, Caseiro R, Martins P, et al. (2012) Exploiting the circulant structure of tracking-by-detection with kernels. European conference on computer vision. Springer Berlin Heidelberg, p. 702–715

  12. Hinterstoisser S, Lepetit V, Ilic S, et al. (2010) Dominant orientation templates for real-time detection of texture-less objects. IEEE conference on computer vision and pattern recognition 10, p.2257–2264

  13. Hong Z, Chen Z, Wang C, Mei X, Prokhorov D, and Tao D. (2015) Multi-store tracker (MUSTer): a cognitive psychology inspired approach to object tracking. IEEE conference on computer vision and pattern recognition, 2015, p. 749–758

  14. Jia X, Lu H, Yang MH (2012) Visual tracking via adaptive structural local sparse appearance model. IEEE conference on computer vision and pattern recognition, p.1822–1829

  15. Kwon J, Lee KM (2010) Visual tracking decomposition. IEEE conference on computer vision and pattern recognition, p1269–1276

  16. Kwon J, Lee KM (2011) Tracking by sampling trackers. International conference on computer vision, p.1195–1202

  17. Kwon J, Lee KM (2013) Highly nonrigid object tracking via patch-based dynamic appearance modeling. IEEE Trans Pattern Anal Mach Intell 35(10):2427–2441

    Article  MathSciNet  Google Scholar 

  18. Li F, Tian C, Zou W, Zhang L, Yang M-H (2018) Learning spatial-temporal regularized correlation filters for visual tracking. IEEE conference on computer vision and pattern recognition

  19. Liu B, Huang J, Yang L, and Kulikowsk C (2011) Robust tracking using local sparse appearance model and K-selection. IEEE conference on computer vision and pattern recognition, p.1313–1320

  20. Liu AA, Su YT, Nie WZ, Kankanhalli M (2017) Hierarchical clustering multi-task learning for joint human action grouping and recognition. IEEE Trans Pattern Anal Mach Intell 39(1):102–114

    Article  Google Scholar 

  21. Liu AA, Xu N, Zhang H et al. (2018) Multi-level policy and reward reinforcement learning for image captioning, twenty-seventh international joint conference on artificial intelligence (IJCAI), p.821-827

  22. Liu L, Yan X, Shen A (2019) Adaptive multi-feature fusion for correlation filter tracking. Commun Signal Process Syst 1:1057–1066

    Article  Google Scholar 

  23. Ma C, Liu C, Peng F, Liu J (2016) Multi-feature hashing tracking. Pattern Lett 69:62–71

    Article  Google Scholar 

  24. Maresca ME, Petrosino A, Matrioska (2013) A multi-level approach to fast tracking by learning. International Conference on Image Analysis and Processing. Springer Berlin Heidelberg, p.419–428

  25. Ning J, Yang J, Jiang S, Zhang L and Yang MH (2016) Object tracking via dual linear structured SVM and explicit feature map. IEEE conference on computer vision and pattern recognition, p.4266-4274

  26. Oron S, Bar-Hillel A, Levi D, Avidan S (2012) Locally orderless tracking. IEEE conference on computer vision and pattern recognition, p.1940–1947

  27. Pearson K, Galton F (2014) Pearson product-moment correlation coefficient. Covariance

  28. Ross DA, Lim J, Lin RS, Yang MH (2008) Incremental learning for robust visual tracking. Int J Comput Vis 77(1–3):125–141

    Article  Google Scholar 

  29. Sevilla-Lara L and Learned-Miller E (2012) Distribution fields for tracking. IEEE conference on computer vision and pattern recognition, p.1910–1917

  30. Song Y, Ma C, Gong L, Zhang J, Lau RWH, Yang M-H (2017) CREST: convolutional residual learning for visual tracking. International conference on computer vision, p.2555–2563

  31. Stadler S, Grabner H, Van Gool L (2012) Dynamic objectness for adaptive tracking. Conference Asian Conference on Computer Vision, p43–56

  32. Tu WC, He S, Yang Q, Chien SY (2016) Real-time salient object detection with a minimum spanning tree. IEEE conference on computer vision and pattern recognition, p.2334–2342

  33. Valmadre J, Bertinetto L, Henriques J, Vedaldi A, Torr PHS (2017) End-to-end representation learning for correlation filter based tracking. IEEE conference on computer vision and pattern recognition, p.5000-5008

  34. Vojíř T, Matas J (2014) The enhanced flock of trackers. Registration and Recognition in Images and Videos. Springer Berlin Heidelberg, p.113–136

  35. Wang D, Lu H, Yang MH (2013) Least soft-threshold squares tracking. IEEE conference on computer vision and pattern recognition. P.2371–2378

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

  37. Xu N, Liu AA, Wong Y, Zhang Y, Nie WZ, Su YT, Kankanhalli M (2019) Dual-stream recurrent neural network for video captioning. IEEE Trans Circuits Syst Video Technology 29(8), p.2482–2493

  38. Zhang L, Van Der Maaten L (2014) Preserving structure in model-free tracking. IEEE Trans Pattern Anal Mach Intell 36(4):756–769

    Article  Google Scholar 

  39. Zhang K, Zhang L, and Yang MH (2012) Real-time compressive tracking. European Conference on Computer Vision, p864–877

  40. Zhang J, Yu J, Tao D (2018) Local deep-feature alignment for unsupervised dimension reduction. IEEE transactions on image processing, p. 1-1

  41. Zhu G, Wang J, Wu Y, et al. (2016) MC-HOG correlation tracking with saliency proposal. Proceedings of the thirtieth AAAI conference on artificial intelligence. AAAI press, p. 3690-3696.

  42. Zhu G, Porikli F, Li H (2016) Beyond local search: tracking objects everywhere with instance -specific proposals. IEEE conference on computer vision and pattern recognition, p.943–951

Download references

Acknowledgments

This paper is funded by some projects of the authors. Yun Liang is the Natural Science Foundation of China (No. 61772209), and the Science and Technology Planning Project of Guangdong Province (No. 2019A050510034, 2019B020219001). Jian Zhang is supported by the Natural Science Foundation of China (No. 61972361). Chen Lin is supported by the Natural Science Foundation of China (No.61472335, 61,972,328). Jun Xiao is supported by the National Natural Science Foundation of China (No.61572431), and the Zhejiang Natural Science Foundation (No.LY17F020009, LR19F020002, LZ17F020001).

Author information

Authors and Affiliations

  1. College of Mathematics and Informatics, South China Agriculture University, Guangzhou, 510642, China

    Yun Liang & Mei-hua Wang

  2. School of Science and Technology, Zhejiang International Studies University, Hangzhou, 310023, China

    Jian Zhang

  3. Department of Computer Science, Xiamen University, Xiamen, 361005, China

    Chen Lin

  4. College of Computer Science & Technology, Zhejiang University, Hangzhou, 310027, China

    Jun Xiao

Authors
  1. Yun Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jian Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mei-hua Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chen Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jun Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jian Zhang.

Additional information

Publisher’s note

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

CLC number: TP 37

Appendix 1: All The Precision Plots and Success Plots

Appendix 1: All The Precision Plots and Success Plots

Fig. 11
figure 11

The total precision plots and success plots

Full size image
Fig. 12
figure 12figure 12figure 12

The total precision plots and success plots

Full size image

1.1 Appendix 2 The comparisons of total 25 videos

Fig. 13
figure 13

comparisons of eight videos.

Full size image
Fig. 14
figure 14

Comparisons of seven videos

Full size image
Fig. 15
figure 15

Comparisons of seven videos.

Full size image
Fig. 16
figure 16

Comparisons of three videos

Full size image

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liang, Y., Zhang, J., Wang, Mh. et al. Multi-features guided robust visual tracking. Multimed Tools Appl 80, 16367–16395 (2021). https://doi.org/10.1007/s11042-020-08791-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-08791-z

Keywords

Search

Navigation