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Multimedia Tools and Applications

, Volume 78, Issue 22, pp 31633–31648 | Cite as

Robust visual tracking via a hybrid correlation filter

  • Yong Wang
  • Xinbin LuoEmail author
  • Lu Ding
  • Jingjing Wu
  • Shan Fu
Article
  • 42 Downloads

Abstract

In this paper, we propose a hybrid correlation filter based tracking method which depends on coupled interactions between a global filter and two local filters. Specifically, a local kernel feature with Gaussian curvature is developed to encode object appearance. Then the global filter and the two local filters independently track the target. The peak-to-sidelobe ratio (PSR) is employed to measure the reliability of the tracking results. Next, the global filter and the two local filters jointly determine the target position. In this way, the proposed hybrid model deals well with challenging situations, e.g., partial occlusion and scale changes. Experiments on large benchmark datasets show that our method performs favorably against state-of-the-art trackers.

Keywords

Correlation filter based tracking Global filter Local filter Gaussian curvature Peak-to-sidelobe ratio 

Notes

Acknowledgments

This paper is jointly supported by the National Natural Science Foundation of China (61305016) and Fundamental Research Funds for the Central Universities (GrantNo.JUSRP1059). We thank the anonymous editor and reviewers for their careful reading and many insightful comments and suggestions.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Aeronautics and AstronauticsShanghai Jiao Tong UniversityShanghaiChina
  2. 2.School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada
  3. 3.School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina
  4. 4.School of Mechanical EngineeringJiangnan UniversityWuxiChina

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