Skip to main content
SpringerLink
Log in

On large appearance change in visual tracking

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This paper concerns on overcoming the challenges caused by drastic appearance change in visual tracking, especially the long-term appearance variation due to occlusion or large object deformation. We aim to build a long-term appearance model for robust tracking against large appearance change in two new respects: using historical and distinguishing cues to model target representation and extracting effective spatial objectness features from each frame to distinguish outliers. For the first purpose, an adaptive superpixel-based appearance model is formulated. Different from previous superpixel-based trackers, a complementary feature set is defined for the update model to preserve the features of those temporally disappeared object parts especially under occlusion and large deformation. For the second purpose, three new spatial objectness cues specially designed for tracking are defined, including surrounding comparison, edge density change and weighted superpixel straddling. With these spatial objectness cues, our method facilitates target object localization and ensures the target has similar edge distribution between adjacent frames. These cues greatly improve the ability of our method to distinguish the target from its surrounding background. The adaptive appearance model retains valuable features of historical results, and the spatial objectness cues are extracted from the current frame, and thus they are finally combined to complement with each other to solve large appearance changes. The extensive evaluations on the CVPR 2013 online object tracking benchmark and VOT 2014 datasets demonstrate the effectiveness of our method as compared with related trackers.

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
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Smeulders AWM, Chu DM, Cucchiara R, Calderara S, Dehghan A, Shah M (2014) Visual tracking: an experiment survey. IEEE Trans Pattern Anal Mach Intell 36(7):1442–1468

    Article  Google Scholar 

  2. 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: IEEE international conference on computer vision and pattern recognition, pp 749–758

  3. Duffner S, Garcia C (2013) PixelTrack: a fast adaptive algorithm for tracking non-rigid objects. In: IEEE international conference on computer vision, pp 2480–2487

  4. Bibi A, Mueller M, Ghanem B (2016) Target response adaptation for correlation filter tracking. In: European conference on computer vision (2016)

  5. Ma C, Yang X, Zhang C, Yang MY (2015) Long-term correlation tracking. In: IEEE international conference on computer vision and pattern recognition, pp 5388–5396

  6. Zhang S, Zhou H, Jiang F, Li X (2015) Robust visual tracking using structurally random projection and weighted least squares. IEEE Trans Circuits Syst Video Technol 25(11):1749–1760

    Article  Google Scholar 

  7. Wang D, Lu H, Yang MH (2012) Online object tracking with sparse prototypes. IEEE Trans Image Process 22(1):314–325

    Article  MathSciNet  MATH  Google Scholar 

  8. Possegger H, Mauthner T, Bischof H (2015) In defense of color-based model-free tracking. In: IEEE international conference on computer vision and pattern recognition, pp 2113–2120

  9. Danelljan M, Khan FS, Felsberg M, Weijer JV (2014) Adaptive color attributes for real-time visual tracking. In: IEEE international conference on computer vision and pattern recognition, pp 1090–1097

  10. Wang S, Lu H, Yang F, Yang MH (2011) Superpixel tracking. In: IEEE international conference on computer vision, pp 1323–1330

  11. Wen LY, Cai ZW, Lei Z, Yi D, Li SZ (2014) Robust online learned spatio-temporal context model for visual tracking. IEEE Trans Image Process 23(2):785–796

    Article  MathSciNet  MATH  Google Scholar 

  12. Kwon J, Roh J, Lee KM, Gool LV (2014) Robust visual tracking with double bounding box. In: European conference on computer vision, pp 377–392

  13. Zhang K, Zhang L, Yang MH (2014) Fast compressive tracking. IEEE Trans Pattern Anal Mach Intell 36(10):2002–2015

    Article  Google Scholar 

  14. Danelljan M, Hager G, Khan FS, Felsberg M (2016) Discriminative scale space tracking. IEEE Trans Pattern Anal Mach Intell 39:1561–1575

    Article  Google Scholar 

  15. Li X, Dick A, Shen C, van den Hengel A, Wang H (2013) Incremental learning of 3D-DCT compact representations for robust visual tracking. IEEE Trans Pattern Anal Mach Intell 35(4):863–881

    Article  Google Scholar 

  16. Bai QX, Wu Z, Sclaroff S, Betke M, Monnier C (2013) Randomized ensemble tracking. In: IEEE international conference on computer vision, pp 2040–2047

  17. Santner J, Leistner C, Saffari A, Pock T, Bischof H (2010) PROST: Parallel robust online simple tracking. In: IEEE international conference on computer vision and pattern recognition, pp 723–730

  18. Zhong W, Lu H, Yang MH (2012) Robust object tracking via sparsity based collaborative model. In: IEEE international conference on computer vision and pattern recognition, pp 1838–1845

  19. Wang D, Lu HC (2014) Visual tracking via probability continuous outlier model. In: IEEE international conference on computer vision and pattern recognition, pp 3478–3485

  20. Atkinson RC, Shiffrin RM (1968) Human memory: a proposed system and its control processes. Psychol Learn Motiv 2:89–195

    Article  Google Scholar 

  21. Wang N, Shi J, Yeung DY, Jia J (2015) Understanding and diagnosing visual tracking systems. In: IEEE international conference on computer vision and pattern recognition, pp 3101–3109

  22. Kwon J, Lee KM (2009) Tracking of a non-rigid object via patch-based dynamic appearance modeling and adaptive Basin Hopping Monte Carlo sampling. In: IEEE international conference on computer vision and pattern recognition, pp 1208–1215

  23. Li C, Cheng H, Hu S, Liu X, Tang J, Lin L (2016) Learning collaborative sparse representation for grayscale-thermal tracking. IEEE Trans Image Process 25(12):5743–5756

    Article  MathSciNet  MATH  Google Scholar 

  24. Lan XY, Ma AJ, Yuen PC (2014) Multi-cue visual tracking using robust feature-level fusion based on joint sparse representation. In: IEEE international conference on computer vision and pattern recognition, pp 1194–1201

  25. Zhang T, Liu S, Xu C, Yan S, Ghanem B, Ahuja N, Yang MH (2015) Structural sparse tracking. In: IEEE international conference on computer vision and pattern recognition, pp 150–158

  26. Chen DP, Yuan ZJ, Wu Y, Zhang G, Zheng NJ (2013) Constructing adaptive complex cells for robust visual tracking. In: IEEE international conference on computer vision, pp 1113–1120

  27. Dai M, Cheng S, He X, Wang D (2018) Object tracking in the presence of shaking motions. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3387-3

    Article  Google Scholar 

  28. Li C, Lin L, Zuo W, Tang J, Yang MH (2018) Visual tracking via dynamic graph learning. In: IEEE transactions on pattern analysis and machine intelligence, pp 1–15

  29. Zhong W, Lu HC, Yang MH (2012) Robust object tracking via sparsity-based collaborative model. In: IEEE international conference on computer vision and pattern recognition, pp 1838–1845

  30. Sun S, An Z, Jiang X, Zhang B, Zhang J (2018) Robust object tracking with the inverse relocation strategy. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3667-y

    Article  Google Scholar 

  31. 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. In: IEEE conference on computer vision and pattern recognition, pp 479–488

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

  33. Hare S, Saffari A, Torr PHS (2011) Struck: structured output tracking with kernels. In: IEEE international conference on computer vision, pp 263–270

  34. Li Y, Zhu J, Hoi SCH (2015) Reliable patch trackers: robust visual tracking by exploiting reliable patches. In: IEEE international conference on computer vision and pattern recognition, pp 353–361

  35. Wang D, Lu H, Xiao Z, Yang MH (2015) Inverse sparse tracker with a locally weighted distance metric. IEEE Trans Image Process 24(9):2646–2657

    Article  MathSciNet  MATH  Google Scholar 

  36. Wang NY, Wang JD, Yeung DY (2013) Online robust non-negative dictionary learning for visual tracking. In: IEEE international conference on computer vision, pp 657–664

  37. 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 

  38. Godec M, Roth PM, Bischof H (2011) Hough-based tracking of non-rigid objects. In: IEEE international conference on computer vision, pp 81–88

  39. Hu W, Zhou X, Hu M, Maybank S (2009) Occlusion reasoning for tracking multiple walking people. IEEE Trans Circuits Syst Video Technol 19(1):114–121

    Article  Google Scholar 

  40. Hu M, Liu Z, Zhang J, Zhang G (2017) Robust object tracking via multi-cue fusion. Signal Process 139:86–95

    Article  Google Scholar 

  41. Grabner H, Leistner C, Bischof H (2008) Semi-supervised on-line boosting for robust tracking. In: European conference on computer vision, pp 234–247

  42. Zhang H, Cao X, Ho JKL, Chow TWS (2017) Object-level video advertising: an optimization framework. IEEE Trans Ind Inform 13:520–531

    Article  Google Scholar 

  43. Kadir T, Zisserman A, Brady M (2004) An affine invariant salient region detector. In: European conference on computer vision, pp 228–241

  44. Marchesotti L, Cifarelli C, Csurka G (2009) A framework for visual saliency detection with applications to image thumb nailing. In: IEEE international conference on computer vision, pp 2232–2239

  45. Hou X, Zhang L (2007) Saliency detection: a spectral residual approach. In: IEEE international conference on computer vision and pattern recognition, pp 1–8

  46. Alexe B, Deselaers T, Ferrari V (2012) Measuring the objectness of image windows. IEEE Trans Pattern Anal Mach Intell 34(11):2189–2202

    Article  Google Scholar 

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

  48. Xing JL, Gao J, Li B, Hu WM, Yan SC (2013) Robust object tracking with online multi-lifespan dictionary learning. In: IEEE international conference on computer vision, pp 665–672

  49. Radhakrishna A, Shaji A, Lucchi K, Fua P, Susstrunk S (2010) Slic-superpixels, No. EPFL-REPORT-149300

  50. Kristan M, Pflugfelder R, et al (2014) The visual object tracking VOT2014 challenge results. In: European conference on computer vision (Workshop, 2014)

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

Download references

Acknowledgements

Many thanks go to the anonymous reviewers for their careful work and thoughtful suggestions that have helped us to achieve great and substantial improvement on this paper. This work was partially supported by the National Natural Science Fund of China (61772209, 61772257, 61672279), the Science and Technology Planning Project of Guangdong Province (2016A050502050). Wei-shi Zheng is the corresponding author of this paper.

Author information

Authors and Affiliations

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

    Yun Liang & Mei-hua Wang

  2. National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Yan-wen Guo

  3. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China

    Wei-shi Zheng

Authors
  1. Yun Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mei-hua Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yan-wen Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wei-shi Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wei-shi Zheng.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liang, Y., Wang, Mh., Guo, Yw. et al. On large appearance change in visual tracking. Neural Comput & Applic 32, 6089–6109 (2020). https://doi.org/10.1007/s00521-019-04094-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-019-04094-z

Keywords

Search

Navigation