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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)

Abstract

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.

Keywords

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.

Notes

Acknowledgement

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).

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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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