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Set to Set Visual Tracking

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 9810)

Abstract

Sparse representation has been widely used in visual tracking and achieves superior tracking results. However, most sparse representation models represent the target candidate as a linear combination of target templates and need to solve a sparse optimization problem. In this paper, we propose a novel set to set visual tracking (SSVT) method. Under the particle filter framework, we consider both the target candidates and target templates as image sets, and model them as convex hulls. Then the distance between two image sets is minimized and the tracking result is the target candidate with the maximum coefficient. As the target candidates are modeled as one convex hull, SSVT utilizes the underlying relationship of the target candidates. Moreover, SSVT is very efficient in that it only needs to solve one quadratic optimization problem rather than sparse optimization problems. Both qualitative and quantitative analyses on several challenging image sequences show that the proposed SSVT algorithm outperforms the state-of-the-art trackers.

Keywords

  • Set to set distance
  • Visual tracking
  • Particle filter
  • Convex hull
  • Support vector machine

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References

  1. Hong, Z., Mei, X., Prokhorov, D., Tao, D.: Tracking via robust multi-task multi-view joint sparse representation. In: ICCV (2013)

    Google Scholar 

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

    Google Scholar 

  3. Liu, B., Huang, J., Kulikowski, C., Yang, L.: Robust visual tracking using local sparse appearance model and k-selection. TPAMI 35(12), 2968–2981 (2013)

    CrossRef  Google Scholar 

  4. Mei, X., Ling, H., Wu, Y., Blasch, E., Bai, L.: Minimum error bounded efficient \(l_1\) tracker with occlusion detection. In: CVPR (2011)

    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. Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. TPAMI 37(3), 583–596 (2015)

    CrossRef  Google Scholar 

  7. Zhang, K., Liu, Q., Wu, Y., Yang, M.H.: Robust visual tracking via convolutional networks without training. TIP 25(4), 1779–1792 (2016)

    MathSciNet  Google Scholar 

  8. Zhou, Y., Bai, X., Liu, W., Latecki, L.J.: Similarity fusion for visual tracking. IJCV, 1–27 (2016)

    Google Scholar 

  9. Babenko, B., Yang, M.H., Belongie, S.: Robust object tracking with online multiple instance learning. TPAMI 33(8), 1619–1632 (2011)

    CrossRef  Google Scholar 

  10. Mei, X., Ling, H.: Robust visual tracking and vehicle classification via sparse representation. TPAMI 33(11), 2259–2272 (2011)

    CrossRef  Google Scholar 

  11. Grabner, H., Grabner, M., Bischof, H.: Real-time tracking via on-line boosting. In: BMVC (2006)

    Google Scholar 

  12. Grabner, H., Leistner, C., Bischof, H.: Semi-supervised on-line boosting for robust tracking. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 234–247. Springer, Heidelberg (2008)

    CrossRef  Google Scholar 

  13. Avidan, S.: Ensemble tracking. TPAMI 29(2), 261–271 (2007)

    CrossRef  Google Scholar 

  14. Zhang, K., Zhang, L., Liu, Q., Zhang, D., Yang, M.-H.: Fast visual tracking via dense spatio-temporal context learning. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part V. LNCS, vol. 8693, pp. 127–141. Springer, Heidelberg (2014)

    Google Scholar 

  15. Black, M.J., Jepson, A.D.: Eigentracking: robust matching and tracking of articulated objects using a view-based representation. IJCV 26(1), 63–84 (1998)

    CrossRef  Google Scholar 

  16. Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: CVPR (2000)

    Google Scholar 

  17. Adam, A., Rivlin, E., Shimshoni, I.: Robust fragments-based tracking using the integral histogram. In: CVPR (2006)

    Google Scholar 

  18. Ross, D.A., Lim, J., Lin, R.S., Yang, M.H.: Incremental learning for robust visual tracking. IJCV 77(1–3), 125–141 (2008)

    CrossRef  Google Scholar 

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

    Google Scholar 

  20. 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)

    CrossRef  Google Scholar 

  21. Zhang, T., Ghanem, B., Liu, S., Ahuja, N.: Low-rank sparse learning for robust visual tracking. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VI. LNCS, vol. 7577, pp. 470–484. Springer, Heidelberg (2012)

    CrossRef  Google Scholar 

  22. Zhang, T., Liu, S., Ahuja, N., Yang, M.H., Ghanem, B.: Robust visual tracking via consistent low-rank sparse learning. IJCV 111(2), 171–190 (2015)

    CrossRef  Google Scholar 

  23. Zhang, T., Ghanem, B., Liu, S., Xu, C., Ahuja, N.: Robust visual tracking via exclusive context modeling. IEEE Trans. Cybern. 46(1), 51–63 (2016)

    CrossRef  Google Scholar 

  24. Bao, C., Wu, Y., Ling, H., Ji, H.: Real time robust L1 tracker using accelerated proximal gradient approach. In: CVPR (2012)

    Google Scholar 

  25. Zhang, T., Liu, S., Xu, C., Yan, S., Ghanem, B., Ahuja, N., Yang, M.H.: Structural sparse tracking. In: CVPR (2015)

    Google Scholar 

  26. Burges, D., Crisp, C.: A geometric interpretation of v\(-\)SVM classifiers. In: NIPS (2000)

    Google Scholar 

  27. Coleman, T.F., Li, Y.: A reflective newton method for minimizing a quadratic function subject to bounds on some of the variables. SIAM J. Optim. 6(4), 1040–1058 (1996)

    MathSciNet  CrossRef  MATH  Google Scholar 

  28. 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)

    CrossRef  Google Scholar 

  29. Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: Exploiting the circulant structure of tracking-by-detection with kernels. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part IV. LNCS, vol. 7575, pp. 702–715. Springer, Heidelberg (2012)

    CrossRef  Google Scholar 

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Acknowledgement

This work was supported by the National Program on Key Basic Research Project under Grant 2013CB329304, the National Natural Science Foundation of China under Grants 61502332, 61432011, 61222210.

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Correspondence to Pengfei Zhu .

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Zhu, W., Zhu, P., Hu, Q., Zhang, C. (2016). Set to Set Visual Tracking. In: Booth, R., Zhang, ML. (eds) PRICAI 2016: Trends in Artificial Intelligence. PRICAI 2016. Lecture Notes in Computer Science(), vol 9810. Springer, Cham. https://doi.org/10.1007/978-3-319-42911-3_59

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  • DOI: https://doi.org/10.1007/978-3-319-42911-3_59

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