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A robust visual tracking method via local feature extraction and saliency detection

  • Yong Wang
  • Xian WeiEmail author
  • Lu Ding
  • Xiaoliang Tang
  • Huanlong Zhang
Original Article
  • 60 Downloads

Abstract

Visual object tracking is a fundamental problem in computer vision. It heavily relies on feature description for the appearance of object. In this paper, we present a robust algorithm which exploits the locally adaptive regression kernel (LARK) feature for visual tracking. The proposed approach formulates the LARK feature in a tracking by detection framework. In addition, we compute a target-specific saliency map as LARK feature with the guidance of the tracking framework. The tracking problem is solved by maximizing an object location likelihood function. We adopt Fast Fourier Transform for fast learning and detection in this work. Extensive experimental results on challenging videos show that the proposed algorithm performs favorably against state-of-the-art methods in terms of accuracy and robustness.

Keywords

Visual object tracking Locally adaptive regression kernel Correlation filter tracking Saliency detection 

Notes

Compliance with ethical standards

Conflicts of interest

We thank the anonymous editor and reviewers for their careful reading and many insightful comments and suggestions. All the authors declare that we have no conflict of interest.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada
  2. 2.Fujian Institute of Research on the Structure of MatterChinese Academy of Sciences (CAS)FuzhouChina
  3. 3.School of Aeronautics and AstronauticsShanghai Jiao Tong UniversityShanghaiChina
  4. 4.College of Electric and Information EngineeringZhengzhou University of Light IndustryZhengzhouChina

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