Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Robust object tracking using a sparse coadjutant observation model

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

Abstract

This paper develops a classical visual tracker that is called a discriminative sparse similarity (DSS) tracker. Based on the classical Laplacian multi-task reverse sparse representation to get a DSS map in the DSS tracker, we introduce a sparse generative model (SGM) to handle the appearance variation in the DSS tracker. With the alliance of the DSS map and the SGM, our proposed method can track the object under the occlusion and appearance variations effectively. Numerous experiments on various challenging videos of a tracking benchmark illustrate that the proposed tracker performs favorably against several state-of-the-art trackers.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

References

  1. Adam A, Rivlin E, Shimshoni I (2006) Robust fragments-based tracking using the integral histogram. IEEE Conf Comput Vis Pattern Recogn 2006:798–805

    Google Scholar 

  2. Avidan S (2007) Ensemble tracking. IEEE Trans Pattern Anal Mach Intell 29(2):261–271

    Google Scholar 

  3. Babenko B, Yang MH, Belongie S (2009) Visual tracking with online multiple instance learning. IEEE Comput Vis Pattern Recogn 2009:983–990

    Google Scholar 

  4. Cui J, Liu Y, Xu Y et al (2013) Tracking generic human motion via fusion of low-and high-dimensional approaches. IEEE Trans Syst Cybern Part B 43(4):996–1002

    Google Scholar 

  5. Danelljan M, Khan FS, Felsberg M et al (2014) Adaptive color attributes for real-time visual tracking. IEEE Conf Comput Vis Pattern Recogn 2014:1090–1097

    Google Scholar 

  6. Danelljan M, Häger G, Khan FS et al (2017) Discriminative scale space tracking. IEEE Trans Pattern Anal Mach Intell 39(8):1561–1575

    Google Scholar 

  7. Dinh TB, Medioni G (2011) Co-training framework of generative and discriminative trackers with partial occlusion handling. IEEE Work Appl Comput Vis 2011:642–649

    Google Scholar 

  8. Gao S, Tsang WH, Chia L T et al (2010) Local features are not lonely- Laplacian sparse coding for image classification. IEEE Comput Vis Pattern Recogn 2010:3555–3561

    Google Scholar 

  9. Hare S, Saffari A, Torr PHS (2012) Struck: structured output tracking with kernels. IEEE Int Conf Comput Vis 2012:263–270

    Google Scholar 

  10. Henriques JF, Rui C, Martins P et al (2014) High-speed tracking with kernelized correlation filters. IEEE Trans Pattern Anal Mach Intell 37(3):583–596

    Google Scholar 

  11. Hu W, Li X, Zhang X et al (2011) Incremental tensor subspace learning and its applications to foreground segmentation and tracking. IEEE Int J Comput Vis 91 (3):303–327

    MATH  Google Scholar 

  12. Ji H, Ling H, Wu Y et al (2012) Real time robust \(l_{1}\) tracker using accelerated proximal gradient approach. IEEE Comput Vis Pattern Recogn 2012:1830–1837

    Google Scholar 

  13. Kalal Z, Matas J, Mikolajczyk K (2010) P-N Learning: bootstrapping binary classifiers by structural constraints. IEEE Comput Vis Pattern Recogn 2010:49–56

    Google Scholar 

  14. Kalal Z, Mikolajczyk K, Matas J (2012) Tracking-learning-detection. IEEE Trans Pattern Anal Mach Intell 34(7):1409–1422

    Google Scholar 

  15. Kwon J, Lee KM (2010) Visual tracking decomposition. IEEE Comput Vis Pattern Recogn 2010:1269–1276

    Google Scholar 

  16. Li X, Hu W, Shen C et al (2013) A survey of appearance models in visual object tracking. ACM Trans Intell Syst Technol 4(4):58

    Google Scholar 

  17. Liu B, Huang J, Yang L et al (2011) Robust tracking using local sparse appearance model and K-selection. IEEE Comput Vis Pattern Recogn 2011:1313–1320

    Google Scholar 

  18. Liu Y, Cui J, Zhao H et al (2012) Fusion of low-and high-dimensional approaches by trackers sampling for generic human motion tracking. Int Conf Pattern Recogn 2012:898–901

    Google Scholar 

  19. Liu Y, Nie L, Han L et al (2015) Action2activity: recognizing complex activities from sensor data. Int Conf Artif Intell 2015:1617–1623

    Google Scholar 

  20. Liu L, Cheng L, Liu Y et al (2016) Recognizing complex activities by a probabilistic interval-based model. Thirtieth Aaai Conf Artif Intell 2016:1266–1272

    Google Scholar 

  21. Liu Y, Nie L, Li L et al (2016) From action to activity: sensor-based activity recognition. Neurocomputing 181:108–115

    Google Scholar 

  22. Lu H, Jia X, Yang MH (2012) Visual tracking via adaptive structural local sparse appearance model. IEEE Comput Vis Pattern Recogn 2012:1822–1829

    Google Scholar 

  23. Mei X, Ling H (2009) Robust visual tracking using \(l_{1}\) minimization. IEEE Int Conf Comput Vis 2009:1436–1443

    Google Scholar 

  24. Mei X, Ling H, Wu Y et al (2011) Minimum error bounded efficient \(l_{1}\) tracker with occlusion detection. IEEE Comput Vis Pattern Recogn 2011:1257–1264

    Google Scholar 

  25. Ross DA, Lim J, Lin RS et al (2008) Incremental learning for robust visual tracking. Int J Comput Vis 77(1-3):125–141

    Google Scholar 

  26. Rui C, Martins P, Batista J (2012) Exploiting the circulant structure of tracking-by-detection with kernels. IEEE Eur Conf Comput Vis 2012:702–715

    Google Scholar 

  27. Santner J, Leistner C, Saffari A et al (2010) PROST: Parallel robust online simple tracking. IEEE Comput Vis Pattern Recogn 2010:723–730

    Google Scholar 

  28. Tseng P (2008) On accelerated proximal gradient methods for convex-concave optimization. SIAM J Optim

  29. Wang D, Lu H, Chen YW (2010) Incremental MPCA for color object tracking. IEEE Int Conf Pattern Recogn 2010:1751–1754

    Google Scholar 

  30. Wang J, Yang J, Yu K et al (2010) Locality-constrained linear coding for image classification. IEEE Comput Vis Pattern Recogn 2010:3360–3367

    Google Scholar 

  31. Wang S, Lu H, Yang F et al (2011) Superpixel tracking. IEEE Int Conf Comput Vis 2011:1323–1330

    Google Scholar 

  32. Wang Q, Yang MH (2012) Online discriminative object tracking with local sparse representation. Appl Comput Vis 2012:425–432

    Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  34. Wu Y, Lim J, Yang MH (2013) Online object tracking: a benchmark. IEEE Comput Vis Pattern Recogn 2013:2411–2418

    Google Scholar 

  35. Yang J, Yu K, Gong Y et al (2009) Linear spatial pyramid matching using sparse coding for image classification. IEEE Comput Vis Pattern Recogn 2009:1794–1801

    Google Scholar 

  36. Yang L, Yang L, Huang J et al (2010) Robust and fast collaborative tracking with two stage sparse optimization. IEEE Eur Conf Comput Vis 2010:624–637

    Google Scholar 

  37. Zhang T, Ghanem B, Liu S et al (2012) Robust visual tracking via multi-task sparse learning. IEEE Comput Vis Pattern Recogn 2012:2042–2049

    Google Scholar 

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

    Google Scholar 

  39. Zhang T, Liu S, Xu C et al (2015) Structural sparse tracking. IEEE Comput Vis Pattern Recogn 2015:150–158

    Google Scholar 

  40. Zhang K, Liu Q, Wu Y et al (2016) Robust visual tracking via convolutional networks without training. IEEE Trans Image Process 25(4):1779–1792

    MathSciNet  MATH  Google Scholar 

  41. Zhong W, Lu H, Yang M (2014) Robust object tracking via sparsity based collaborative model. IEEE Trans Image Process 23(5):2356–2368

    MathSciNet  MATH  Google Scholar 

  42. Zhuang B, Lu H, Xiao Z et al (2014) Visual tracking via discriminative sparse similarity map. IEEE Trans Image Process 23(4):1872–1881

    MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

This work was funded by the National Natural Science Foundation of China (61571410 and 61672477) and the Zhejiang Provincial Nature Science Foundation of China (LY18F020018).

Author information

Authors and Affiliations

  1. Department of Information Sciences and Mathematics, China Jiliang University, Hangzhou, 310018, Zhejiang Province, People’s Republic of China

    Jianwei Zhao, Weidong Zhang & Feilong Cao

Authors
  1. Jianwei Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weidong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Feilong Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Feilong Cao.

Ethics declarations

Conflict of interests

The authors declare that they have no conflict of interest.

Research involving Human Participants and/or Animals

This study did not involve Human Participants and Animals.

Informed Consent

The all authors of this paper have consented the submission.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, J., Zhang, W. & Cao, F. Robust object tracking using a sparse coadjutant observation model. Multimed Tools Appl 77, 30969–30991 (2018). https://doi.org/10.1007/s11042-018-6132-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6132-0

Keywords

Search

Navigation