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Robust Object Tracking Based on Multi-granularity Sparse Representation

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Intelligence Science and Big Data Engineering. Visual Data Engineering (IScIDE 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11935))

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Abstract

Even though recent advances in object tracking have shown notable results in tracking efficiency, many of these algorithms are not powerful enough for its adaptation to appearance changes caused by intrinsic and extrinsic factors. In this paper, a robust object tracking method based on multi-granularity sparse representation has been proposed to exploit not only the effectiveness of holistic and local features but also make use of the representation ability of multiple patches under different granularity. For the first part, contour templates have been introduced to combine with PCA basis vectors and square templates to enhance the observation model’s ability to resist the appearance changes of the target. For the second part, a novel block-division scheme is designed for multi-granularity sparsity analysis, which takes into account the joint representation ability of the target patches with different sizes. At last, in order to reduce tracking model’s drift phenomenon due to model update, an adaptive update mechanism is designed by combining occlusion ratio and incremental HOG feature. Both qualitative and quantitative evaluations have been conducted on OTB-2013 datasets to demonstrate that the proposed tracking algorithm outperforms several state-of-the-art methods.

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References

  1. Mei, X., Ling, H.: Robust visual tracking using ℓ1 minimization. In: 12th International Conference on Computer Vision, pp. 1436–1443 (2009)

    Google Scholar 

  2. Mei, X., Ling, H., Wu, Y., et al.: Minimum error bounded efficient ℓ1 tracker with occlusion detection. In: CVPR, pp. 1257–1264 (2011)

    Google Scholar 

  3. Bao, C., Wu, Y., Ling, H., et al.: Real time robust L1 tracker using accelerated proximal gradient approach. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1830–1837 (2012)

    Google Scholar 

  4. Liu, B., Yang, L., Huang, J., Meer, P., Gong, L., Kulikowski, C.: Robust and fast collaborative tracking with two stage sparse optimization. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 624–637. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15561-1_45

    Chapter  Google Scholar 

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

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  7. Xiao, Z., Lu, H., Wang, D.: L2-RLS-based object tracking. IEEE Trans. Circuits Syst. Video Technol. 24(8), 1301–1309 (2013)

    Article  Google Scholar 

  8. Ross, D.A., Lim, J., Lin, R.S., et al.: Incremental learning for robust visual tracking. Int. J. Comput. Vision 77(1–3), 125–141 (2008)

    Article  Google Scholar 

  9. Hare, S., Golodetz, S., Saffari, A., et al.: Struck: structured output tracking with kernels. IEEE Trans. Pattern Anal. Mach. Intell. 8(10), 206–2109 (2015)

    Google Scholar 

  10. Liu, B., Huang, J., Yang, L., et al.: Robust visual tracking with local sparse appearance model and k-selection. In: CVPR, vol. 1, no. 2, p. 3 (2011)

    Google Scholar 

  11. Jia, X., Lu, H., Yang, M.H.: Visual tracking via adaptive structural local sparse appearance model. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1822–1829 (2012)

    Google Scholar 

  12. Zhang, T., Liu, S., Ahuja, N., et al.: Robust visual tracking via consistent low-rank sparse learning. Int. J. Comput. Vision 111(2), 171–190 (2015)

    Article  Google Scholar 

  13. Felzenszwalb, P.F., Girshick, R.B., McAllester, D., et al.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2009)

    Article  Google Scholar 

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

    Article  Google Scholar 

  15. Nai, K., Li, Z., Li, G., et al.: Robust object tracking via local sparse appearance model. IEEE Trans. Image Process. 27(10), 4958–4970 (2018)

    Article  MathSciNet  Google Scholar 

  16. Qi, Y., Qin, L., Zhang, J., et al.: Structure-aware local sparse coding for visual tracking. IEEE Trans. Image Process. 27(8), 3857–3869 (2018)

    Article  MathSciNet  Google Scholar 

  17. Zhang, T., Ghanem, B., Liu, S., et al.: Robust visual tracking via multi-task sparse learning. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2042–2049 (2012)

    Google Scholar 

  18. Zhou, Y., Li, S., Zhang, D., et al.: Seismic noise attenuation using an online subspace tracking algorithm. Geophys. J. Int. 212(2), 1072–1097 (2017)

    Article  Google Scholar 

  19. Narayanamurthy, P., Vaswani, N.: Provable dynamic robust PCA or robust subspace tracking. IEEE Trans. Inf. Theory 65(3), 1547–1577 (2018)

    Article  MathSciNet  Google Scholar 

  20. Vaswani, N., Bouwmans, T., Javed, S., et al.: Robust subspace learning: robust PCA, robust subspace tracking, and robust subspace recovery. IEEE Signal Process. Mag. 35(4), 32–55 (2018)

    Article  Google Scholar 

  21. Zhang, L., Yang, M., Feng, X.: Sparse representation or collaborative representation: which helps face recognition? In: International Conference on Computer Vision, pp. 471–478 (2011)

    Google Scholar 

  22. Shi, Q., Eriksson, A., Van Den Hengel, A., et al.: Is face recognition really a compressive sensing problem? In: CVPR, pp. 553–560 (2011)

    Google Scholar 

  23. Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2411–2418 (2013)

    Google Scholar 

  24. Zhang, T., Xu, C., Yang, M.H.: Robust structural sparse tracking. IEEE Trans. Pattern Anal. Mach. Intell. 41(2), 473–486 (2018)

    Article  Google Scholar 

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Acknowledgements

This work was supported in part by the Natural Science Foundation of China under Grant 61703283, 61703169, 61806127, in part by the China Postdoctoral Science Foundation under Project 2016M590812, Project 2017T100645 and Project 2017M612736, in part by the Guangdong Natural Science Foundation under Project 2017A030310067, Project 2018A030310450 and Project 2018A030310451, in part by the National Engineering Laboratory for Big Data System Computing Technology, in part by the Guangdong Laboratory of Artificial-Intelligence and Cyber-Economics (SZ), in part by the Scientific Research Foundation of Shenzhen University under Project 2019049 and Project 860-000002110328, in part by the Research Foundation for Postdoctor Worked in Shenzhen under Project 707-00012210.

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Authors and Affiliations

  1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, People’s Republic of China

    Honglin Chu, Jiajun Wen & Zhihui Lai

  2. Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen, 518060, People’s Republic of China

    Jiajun Wen & Zhihui Lai

  3. The National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, 518060, People’s Republic of China

    Jiajun Wen

Authors
  1. Honglin Chu
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  2. Jiajun Wen
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  3. Zhihui Lai
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Corresponding author

Correspondence to Jiajun Wen .

Editor information

Editors and Affiliations

  1. Nanjing University of Science and Technology, Nanjing, China

    Zhen Cui

  2. Nanjing University of Science and Technology, Nanjing, China

    Jinshan Pan

  3. Nanjing University of Science and Technology, Nanjing, China

    Shanshan Zhang

  4. Nanjing University of Science and Technology, Nanjing, China

    Liang Xiao

  5. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

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Chu, H., Wen, J., Lai, Z. (2019). Robust Object Tracking Based on Multi-granularity Sparse Representation. In: Cui, Z., Pan, J., Zhang, S., Xiao, L., Yang, J. (eds) Intelligence Science and Big Data Engineering. Visual Data Engineering. IScIDE 2019. Lecture Notes in Computer Science(), vol 11935. Springer, Cham. https://doi.org/10.1007/978-3-030-36189-1_12

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  • DOI: https://doi.org/10.1007/978-3-030-36189-1_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36188-4

  • Online ISBN: 978-3-030-36189-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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