Blur-Resilient Tracking Using Group Sparsity

  • Pengpeng LiangEmail author
  • Yi Wu
  • Xue Mei
  • Jingyi Yu
  • Erik Blasch
  • Danil Prokhorov
  • Chunyuan Liao
  • Haitao Lang
  • Haibin Ling
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9007)


In this paper, a Blur Resilient target Tracking algorithm (BReT) is developed by modeling target appearance with a groupwise sparse approximation over a template set. Since blur templates of different directions are added to the template set to accommodate motion blur, there is a natural group structure among the templates. In order to enforce the solution of the sparse approximation problem to have group structure, we employ the mixed \(\ell _1+\ell _1/\ell _2\) norm to regularize the model coefficients. Having observed the similarity of gradient distributions in the blur templates of the same direction, we further boost the tracking robustness by including gradient histograms in the appearance model. Then, we use an accelerated proximal gradient scheme to develop an efficient algorithm for the non-smooth optimization resulted from the representation. After that, blur estimation is performed by investigating the energy of the coefficients, and when the estimated target can be well approximated by the normal templates, we dynamically update the template set to reduce the drifting problem. Experimental results show that the proposed BReT algorithm outperforms state-of-the-art trackers on blurred sequences.


Sparse Representation Motion Blur Group Lasso Group Sparsity Template Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported in part by the US NSF Grants IIS-1218156 and IIS-1350521. Wu was supported in part by NSFC under Grants 61005027 and 61370036, and Lang was supported by “Beijing Higher Education Young Elite Teacher Project” (No.YETP0514).


  1. 1.
    Silveira, G.F., Malis, E.: Real-time visual tracking under arbitrary illumination changes. In: CVPR (2007)Google Scholar
  2. 2.
    Adam, A., Rivlin, E., Shimshoni, I.: Robust fragments-based tracking using the integral histogram. In: CVPR (2006)Google Scholar
  3. 3.
    Jia, X., Lu, H., Yang, M.H.: Visual tracking via adaptive structural local sparse appearance model. In: CVPR (2012)Google Scholar
  4. 4.
    Hu, W., Li, X., Zhang, X., Shi, X., Maybank, S.J., Zhang, Z.: Incremental tensor subspace learning and its applications toforeground segmentation and tracking. IJCV 91, 303–327 (2011)CrossRefzbMATHGoogle Scholar
  5. 5.
    Kwon, J., Lee, K.M.: Wang-landau monte carlo-based tracking methods for abrupt motions. PAMI 35, 1011–1024 (2013)CrossRefGoogle Scholar
  6. 6.
    Cho, S., Lee, S.: Fast motion deblurring. ACM Trans. Graph. 28, 145:1–145:8 (2009)CrossRefGoogle Scholar
  7. 7.
    Xu, L., Zheng, S., Jia, J.: Unnatural l0 sparse representation for natural image deblurring. In: CVPR (2013)Google Scholar
  8. 8.
    Jin, H., Favaro, P., Cipolla, R.: Visual tracking in the presence of motion blur. In: CVPR (2005)Google Scholar
  9. 9.
    Dai, S., Yang, M., Wu, Y., Katsaggelos, A.K.: Tracking motion-blurred targets in video. In: ICIP (2006)Google Scholar
  10. 10.
    Wu, Y., Ling, H., Yu, J., Li, F., Mei, X., Cheng, E.: Blurred target tracking by blur-driven tracker. In: ICCV (2011)Google Scholar
  11. 11.
    Bach, F., Jenatton, R., Mairal, J., Obozinski, G.: Convex optimization with sparsity-inducing norms. In: Sra, S., Nowozin, S., Wright, S. (eds.) Optimization for Machine Learning, pp. 19–53. MIT Press, Cambridge (2011)Google Scholar
  12. 12.
    Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. 38, 13 (2006)CrossRefGoogle Scholar
  13. 13.
    Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2411–2418 (2013)Google Scholar
  14. 14.
    Pang, Y., Ling, H.: Finding the best from the second bests-inhibiting subjective bias in evaluation of visual tracking algorithms. In: Proceedings of IEEE International Conference on Computer Vision, pp. 2784–2791 (2013)Google Scholar
  15. 15.
    Kristan, M., Pflugfelder, R., Leonardis, A., Matas, J., Porikli, F., Khajenezhad, A., Salahledin, A., Soltani-Farani, A., Zarezade, A., Petrosino, A., et al.: The visual object tracking vot2013 challenge results. In: IEEE Workshop on visual object tracking challenge (2013)Google Scholar
  16. 16.
    Smeulders, A.W.M., Chu, D.M., Cucchiara, R., Calderara, S., Dehghan, A., Shah, M.: Visual tracking: an experimental survey. IEEE Trans. Pattern Anal. Mach. Intell. 36, 1428–1441 (2014)CrossRefGoogle Scholar
  17. 17.
    Grabner, H., Grabner, M., Bischof, H.: Real-time tracking via on-line boosting. In: BMVC (2006)Google Scholar
  18. 18.
    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) CrossRefGoogle Scholar
  19. 19.
    Babenko, B., Yang, M.H., Belongie, S.: Robust object tracking with online multiple instance learning. PAMI 33, 1619–1632 (2011)CrossRefGoogle Scholar
  20. 20.
    Hare, S., Saffari, A., Torr, P.H.S.: Struck: structured output tracking with kernels. In: ICCV (2011)Google Scholar
  21. 21.
    Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. PAMI 25, 564–577 (2003)CrossRefGoogle Scholar
  22. 22.
    Black, M.J., Jepson, A.D.: Eigentracking: robust matching and tracking of articulated objects using a view-based representation. IJCV 26, 63–84 (1998)CrossRefGoogle Scholar
  23. 23.
    Ross, D.A., Lim, J., Lin, R.S., Yang, M.H.: Incremental learning for robust visual tracking. IJCV 77, 125–141 (2008)CrossRefGoogle Scholar
  24. 24.
    Kwon, J., Lee, K.M.: Visual tracking decomposition. In: CVPR (2010)Google Scholar
  25. 25.
    Mei, X., Ling, H.: Robust visual tracking and vehicle classification via sparse representation. PAMI 33, 2259–2272 (2011)CrossRefGoogle Scholar
  26. 26.
    Bao, C., Wu, Y., Ling, H., Ji, H.: Real time robust l1 tracker using accelerated proximal gradient approach. In: CVPR (2012)Google Scholar
  27. 27.
    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) CrossRefGoogle Scholar
  28. 28.
    Zhang, T., Ghanem, B., Liu, S., Ahuja, N.: Robust visual tracking via structured multi-task sparse learning. IJCV 101, 367–383 (2013)CrossRefMathSciNetGoogle Scholar
  29. 29.
    Doucet, A., De Freitas, N., Gordon, N., et al.: An introduction to sequential Monte Carlo methods. In: Doucet, A., De Freitas, N., Gordon, N. (eds.) Sequential Monte Carlo Methods in Practice. Statistics for Engineering and Information Science, vol. 1, pp. 3–14. Springer, New York (2001) CrossRefGoogle Scholar
  30. 30.
    Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imag. Sci. 2, 183–202 (2009)CrossRefzbMATHMathSciNetGoogle Scholar
  31. 31.
    Liu, J., Ye, J.: Moreau-Yosida regularization for grouped tree structure learning. In: NIPS (2010)Google Scholar
  32. 32.
    Liu, J., Ji, S., Ye, J.: SLEP: Sparse Learning with Efficient Projections. Arizona State University (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Pengpeng Liang
    • 1
    Email author
  • Yi Wu
    • 1
    • 2
  • Xue Mei
    • 3
  • Jingyi Yu
    • 4
  • Erik Blasch
    • 5
  • Danil Prokhorov
    • 3
  • Chunyuan Liao
    • 6
  • Haitao Lang
    • 1
    • 7
  • Haibin Ling
    • 1
  1. 1.Department of Computer and Information SciencesTemple UniversityPhiladelphiaUSA
  2. 2.Jiangsu Key Laboratory of Big Data Analysis TechnologyNanjing University of Information Science and TechnologyNanjingChina
  3. 3.Toyota Research Institute, North AmericaAnn ArborUSA
  4. 4.Department of Computer and Information SciencesUniversity of DelawareNewarkUSA
  5. 5.Air Force Research LabRomeUSA
  6. 6.HiScene Information TechnologiesShanghaiChina
  7. 7.Department of Physics and ElectronicsBeijing University of Chemical TechnologyBeijingChina

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