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Anti-occlusion object tracking based on correlation filter

  • Jun Liu
  • Gang Xiao
  • Xingchen ZhangEmail author
  • Ping Ye
  • Xingzhong Xiong
  • Shengyun Peng
Original Paper
  • 46 Downloads

Abstract

Despite remarkable progress, visual object tracking is still a challenging task as objects usually suffer from significant appearance changes, fast motion, and serious occlusion. In this paper, we propose an anti-occlusion correlation filter-based tracking method (AO-CF) for robust visual tracking. We first propose an occlusion criterion based on continuous response values. Based on the criterion, objects are divided into four categories to adaptively identify the occlusion of objects. Then we propose a new detection condition for detecting proposals. When the occlusion criterion is triggered, the re-detection mechanism is executed and the tracker is commanded to stop, and then the re-detector selects the most reliable proposal to reinitialize the tracker. Experimental results show that our method outperforms other state-of-the-art trackers in terms of both precision rate and success rate on the widely used object tracking benchmark dataset. In addition, AO-CF is able to achieve real-time tracking speed.

Keywords

Visual tracking Circulant matrices Correlation filter Kernel methods Occlusion 

Notes

Acknowledgements

This paper is sponsored by National Program on Key Basic Research Project (2014CB744903), National Natural Science Foundation of China (61973212, 61673270), Shanghai Science and Technology Committee Research Project (17DZ1204304).

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Aeronautics and AstronauticsShanghai Jiao Tong UniversityShanghaiChina
  2. 2.School of Automation and Information EngineeringSichuan University of Science and EngineeringYibinChina
  3. 3.College of Civil EngineeringTongji UniversityShanghaiChina

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