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Asian Conference on Computer Vision

ACCV 2014: Computer Vision -- ACCV 2014 pp 614–629Cite as

Robust Visual Tracking with Dual Group Structure

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9006))

Abstract

The “sparse representation”-based tracking framework generally considers the testing candidates and dictionary atoms individually, thus failing to model the structured information within data. In this paper, we present a robust tracking framework by exploiting the dual group structure of both candidate samples and dictionary templates, and formulate the sparse representation at group level. The similar samples are encoded simultaneously by a few atom groups, which induces the inter-group sparsity, and also each group enjoys different internal sparsity. In this way, not only the potential commonality shared by the related candidates is taken into account, but also the individual differences between samples are reflected. Then we provide an effective optimization method to solve our formulation by two stages: thresholding and computing with the accelerated proximal gradient method. Finally, we embed the dual group structure model into the particle filter framework for visual tracking. Extensive experimental results demonstrate that our tracker achieves favorable performance against the state-of-the-art tracking methods.

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References

  1. Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009)

    Article  Google Scholar 

  2. Mairal, J., Bach, F., Ponce, J., Sapiro, G., Zisserman, A.: Non-local sparse models for image restoration. In: ICCV (2009)

    Google Scholar 

  3. Mei, X., Ling, H.: Robust visual tracking using \(\ell _1\) minimization. In: ICCV(2009)

    Google Scholar 

  4. Wang, D., Lu, H., Chen, Y.: Incremental MPCA for color object tracking. In: ICPR(2010)

    Google Scholar 

  5. Zhang, T., Ghanem, B., Liu, S., Ahuja, N.: Robust visual tracking via multi-task sparse learning. In: CVPR (2012)

    Google Scholar 

  6. Zhuang, B., Lu, H., Xiao, Z., Wang, D.: Visual tracking via discriminative sparse similarity map. IEEE Trans. Image Process. 23, 1872–1881 (2014)

    Article  MathSciNet  Google Scholar 

  7. Babenko, B., Yang, M.H., Belongie, S.: Visual tracking with online multiple instance learning. In: CVPR (2009)

    Google Scholar 

  8. Hare, S., Saffari, A., Torr, P.H.S.: Struck: Structured output tracking with kernels. In: ICCV (2011)

    Google Scholar 

  9. Zhang, K., Zhang, L., Yang, M.-H.: Real-time compressive tracking. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 864–877. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  10. Bao, C., Wu, Y., Ling, H., Ji, H.: Real time robust l1 tracker using accelerated proximal gradient approach. In: CVPR (2012)

    Google Scholar 

  11. Jia, X., Lu, H., Yang, M.: Visual tracking via adaptive structural local sparse appearance model. In: CVPR (2012)

    Google Scholar 

  12. Wang, D., Lu, H., Yang, M.H.: Online object tracking with sparse prototypes. IEEE Trans. Image Process. 22, 314–325 (2013)

    Article  MathSciNet  Google Scholar 

  13. Yuan, M., Lin, Y.: Model selection and estimation in regression with grouped variables. J. Roy. Stat. Soc., Ser. B 68, 49–67 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  14. Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. Roy. Stat. Soc., Ser. B 67, 301–320 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  15. Bengio, S., Pereira, F.C.N., Singer, Y., Strelow, D.: Group sparse coding. In: NIPS (2009)

    Google Scholar 

  16. Chi, Y.T., Ali, M., Rushdi, M., Ho, J.: Affine-constrained group sparse coding and its application to image-based classifications. In: ICCV (2013)

    Google Scholar 

  17. Chi, Y.T., Ali, M., Rajwade, A., Ho, J.: Block and group regularized sparse modeling for dictionary learning. In: CVPR (2013)

    Google Scholar 

  18. Friedman, J., Hastie, T., Tibshirani, R.: A note on the group lasso and sparse group lasso. CoRR (2010)

    Google Scholar 

  19. Elhamifar, E., Vidal, R.: Structured sparse recovery via convex optimization. CoRR (2011)

    Google Scholar 

  20. Zhang, S., Huang, J., Huang, Y., Yu, Y., Li, H., Metaxas, D.N.: Automatic image annotation using group sparsity. In: CVPR (2010)

    Google Scholar 

  21. 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, Part IV. LNCS, vol. 6314, pp. 624–637. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  22. Jenatton, R., Obozinski, G., Bach, F.: Structured sparse principal component analysis. In: AISTATS (2010)

    Google Scholar 

  23. Tibshirani, R.: Regression shrinkage and selection via the lasso. J. Roy. Stat. Soc., Ser. B 58, 267–288 (1994)

    MathSciNet  Google Scholar 

  24. Nesterov, Y.: Gradient methods for minimizing composite functions. Math. Program. 140, 125–161 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  25. Samaria, F.S., Harter, A.C.: Parameterisation of a stochastic model for human face identification. In: ACV (1994)

    Google Scholar 

  26. Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark. In: CVPR (2013)

    Google Scholar 

  27. Ross, D.A., Lim, J., Lin, R.S., Yang, M.H.: Incremental learning for robust visual tracking. Int. J. Comput. Vision 77, 125–141 (2008)

    Article  Google Scholar 

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

    Article  Google Scholar 

  29. Kwon, J., Lee, K.M.: Visual tracking decomposition. In: CVPR (2010)

    Google Scholar 

  30. Wang, N., Wang, J., Yeung, D.Y.: Online robust non-negative dictionary learning for visual tracking. In: ICCV (2013)

    Google Scholar 

  31. He, S., Yang, Q., Lau, R.W., Wang, J., Yang, M.H.: Visual tracking via locality sensitive histograms. In: CVPR (2013)

    Google Scholar 

  32. Zhong, W., Lu, H., Yang, M.: Robust object tracking via sparse collaborative appearance model. IEEE Trans. Image Process. 23, 2356–2368 (2014)

    Article  MathSciNet  Google Scholar 

  33. Yao, R., Shi, Q., Shen, C., Zhang, Y., van den Hengel, A.: Part-based visual tracking with online latent structural learning. In: CVPR (2013)

    Google Scholar 

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Acknowledgement

This work was supported by the Joint Foundation of China Education Ministry and China Mobile Communication Corporation under Grant MCM20122071, and in part by the Fundamental Research Funds for the Central Universities under Grant DUT14YQ101 and the Natural Science Foundation of China under Grant 61472060.

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Authors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Fu Li, Huchuan Lu & Dong Wang

Authors
  1. Fu Li
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  2. Huchuan Lu
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  3. Dong Wang
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Corresponding author

Correspondence to Huchuan Lu .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Bayern, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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Li, F., Lu, H., Wang, D. (2015). Robust Visual Tracking with Dual Group Structure. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9006. Springer, Cham. https://doi.org/10.1007/978-3-319-16817-3_40

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  • DOI: https://doi.org/10.1007/978-3-319-16817-3_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16816-6

  • Online ISBN: 978-3-319-16817-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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