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Visual Tracking Based on Local Model

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Online Visual Tracking

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Abstract

Many effective and efficient tracking methods cannot deal with partial occlusion and background clutter. To address these issues, several local-based models have been employed for designing robust tracking algorithms.

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Notes

  1. 1.

    ©2012 IEEE, Reprinted, with permission, from Ref. [4].

  2. 2.

    ©2015 IEEE, Reprinted, with permission, from Ref. [14].

References

  1. Adam, A., Rivlin, E., Shimshoni, I.: Robust fragments-based tracking using the integral histogram. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 798–805 (2006)

    Google Scholar 

  2. He, K., Sun, J., Tang, X.: Guided image filtering. In: European Conference on Computer Vision, pp. 1–14 (2010)

    Google Scholar 

  3. He, S., Yang, Q., Lau, R.W., Wang, J., Yang, M.H.: Visual tracking via locality sensitive histograms. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2427–2434 (2013)

    Google Scholar 

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

    Google Scholar 

  5. Kwon, J., Lee, K.M.: Tracking of a non-rigid object via patch-based dynamic appearance modeling and adaptive basin hopping Monte Carlo sampling. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1208–1215 (2009)

    Google Scholar 

  6. Li, F., Jia, X., Xiang, C., Lu, H.: Visual tracking with structured patch-based model. Image Vis. Comput. 60, 124–133 (2017)

    Article  Google Scholar 

  7. Liu, B., Huang, J., Yang, L., Kulikowsk, C.: Robust tracking using local sparse appearance model and k-selection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1313–1320 (2011)

    Google Scholar 

  8. Liu, B., Yang, L., Huang, J., Meer, P., Gong, L., Kulikowski, C.A.: Robust and fast collaborative tracking with two stage sparse optimization. In: European Conference on Computer Vision, pp. 624–637 (2010)

    Chapter  Google Scholar 

  9. Liu, T., Wang, G., Yang, Q.: Real-time part-based visual tracking via adaptive correlation filters. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4902–4912 (2015)

    Google Scholar 

  10. Riesenhuber, M., Poggio, T.A.: Hierarchical models of object recognition in cortex. Nat. Neurosci. 2, 1019–1025 (1999)

    Article  Google Scholar 

  11. Ross, D.A., Lim, J., Lin, R., Yang, M.: Incremental learning for robust visual tracking. Int. J. Comput Vis. 77(1–3), 125–141 (2008)

    Article  Google Scholar 

  12. Serre, T., Wolf, L., Bileschi, S.M., Riesenhuber, M., Poggio, T.A.: Robust object recognition with cortex-like mechanisms. IEEE Trans. Pattern Anal. Mach. Intell. 29(3), 411–426 (2007)

    Article  Google Scholar 

  13. Sun, C., Wang, D., Lu, H.: Occlusion-aware fragment-based tracking with spatial-temporal consistency. IEEE Trans. Image Process. 25(8), 3814–3825 (2016)

    Article  MathSciNet  Google Scholar 

  14. Wang, D., Lu, H., Bo, C.: Visual tracking via weighted local cosine similarity. IEEE Trans. Cybern. 45(9), 1838–1850 (2015)

    Article  Google Scholar 

  15. Wang, F., Zhang, J., Guo, Q., Liu, P., Tu, D.: Robust visual tracking via discriminative structural sparse feature. In: Chinese Conference on Image and Graphics Technologies, pp. 438–446 (2015)

    Google Scholar 

  16. Wang, Q., Chen, F., Xu, W., Yang, M.: Online discriminative object tracking with local sparse representation. In: IEEE Workshop on Applications of Computer Vision, pp. 425–432 (2012)

    Google Scholar 

  17. Wu, Y., Lim, J., Yang, M.: Object tracking benchmark. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1834–1848 (2015)

    Article  Google Scholar 

  18. Xu, Y., Wang, J., Li, H., Li, Y., Miao, Z., Zhang, Y.: Patch-based scale calculation for real-time visual tracking. IEEE Trans. Signal Process. 23(1), 40–44 (2016)

    Article  Google Scholar 

  19. Yang, J., Yu, K., Gong, Y., Huang, T.S.: Linear spatial pyramid matching using sparse coding for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1794–1801 (2009)

    Google Scholar 

  20. Yao, R., Shi, Q., Shen, C., Zhang, Y., van den Hengel, A.: Part-based visual tracking with online latent structural learning. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2363–2370 (2013)

    Google Scholar 

  21. Zhong, W., Lu, H., Yang, M.: Robust object tracking via sparsity-based collaborative model. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1838–1845 (2012)

    Google Scholar 

  22. Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. Roy. Stat. Soc. Ser. B (Stat. Methodol.) 67(2), 301–320 (2005)

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Huchuan Lu & Dong Wang

Authors
  1. Huchuan Lu
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  2. Dong Wang
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Correspondence to Huchuan Lu .

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Lu, H., Wang, D. (2019). Visual Tracking Based on Local Model. In: Online Visual Tracking. Springer, Singapore. https://doi.org/10.1007/978-981-13-0469-9_3

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  • DOI: https://doi.org/10.1007/978-981-13-0469-9_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-0468-2

  • Online ISBN: 978-981-13-0469-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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