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Long-term correlation tracking via spatial–temporal context

  • Zhi Chen
  • Peizhong LiuEmail author
  • Yongzhao Du
  • Yanmin Luo
  • Jing-Ming Guo
Original Article
  • 35 Downloads

Abstract

In this paper, we mainly deal with the problems of long-term visual tracking while the target objects undergo sophisticated scenarios such as occlusion, out-of-view, and scale changes. We employ two discriminative correlation filters (DCFs) for achieving long-term object tracking, which is performed by learning a spatial–temporal context correlation filter for translation estimation. As for the scale estimation, which is achieved by learning a scale DCF centered on the estimated target position to estimate scale from the best confident results. In addition, we proposed an efficient model update and redetecting activate strategy to avoid unrecoverable drift due to noisy updates, and achieve robust long-term tracking in the case of tracking failure. We evaluate our algorithm carry on OTB benchmark datasets, and the tracking results of both qualitative and quantitative evaluations on challenging sequences demonstrate that the proposed algorithm performs superiorly against several state-of-the-art DCFs methods including some methods which follow deep learning paradigm.

Keywords

Visual tracking Discriminative correlation filters Spatial–temporal context Long-term tracking 

Notes

Funding

This work was supported by Promotion Program for Young and Middle-aged Teacher in Science and Technology Research of Huaqiao University (No.ZQN-PY518), and the grants from National Natural Science Foundation of China (Grant No.61605048), in part by Natural Science Foundation of Fujian Province, China under Grant 2015J01256, and Grant 2016J01300, in part by Fujian Provincial Big Data Research Institute of Intelligent Manufacturing, in part by the Quanzhou scientific and technological planning projects (No.2018C113R and No. 2017G024), and in part by the Subsidized Project for Postgraduates’ Innovative Fund in Scientific Research of Huaqiao University under Grant 1611422001.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

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Copyright information

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

Authors and Affiliations

  1. 1.College of EngineeringHuaqiao UniversityQuanzhouChina
  2. 2.College of Computer Science and TechnologyHuaqiao UniversityXiamenChina
  3. 3.Research Center of Applied Statistics and Big DataHuaqiao UniversityXiamenChina
  4. 4.Department of Electrical EngineeringNational Taiwan University of Science and TechnologyTaipeiTaiwan

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