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Long-Term Tracking Algorithm Based on Kernelized Correlation Filter

  • Na LiEmail author
  • Lingfeng WuEmail author
  • Daxiang Li
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 891)

Abstract

KCF (Kernelized Correlation Filter) is a classical tracking algorithm based on correlation filter, which has good performance in short-term tracking. But when the object is partially or fully occluded, or disappeared in the view, KCF doesn’t work well. In this paper, a long-term tracking algorithm based on KCF is proposed. HOG (Histogram of Oriented Gradient) and LAB three-channel color information are employed to represent the object, and a re-detection module is added into the KCF tracking procedure. The peak ratio is introduced to control the start of the re-detection module, and a correlation filter model based on SURF feature points is re-learned to continuously track the occluded object. Experimental results on OTB dataset show that our algorithm has higher tracking accuracy than other five trackers, and is suitable for long-term tracking.

Keywords

Long-term object tracking Correlation filter Re-detection 

Notes

Acknowledgments

The work is sponsored by the Shaanxi International Cooperation Exchange Funded Projects (2017KW-013, 2017KW-016), Graduate Creative Funds of Xi’an University of Posts and Telecommunications (CXJJ2017004).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Center for Image and Information ProcessingXi’an University of Posts and TelecommunicationsXi’anChina
  2. 2.Key Laboratory of Electronic Information Application Technology for Scene InvestigationMinistry of Public SecurityXi’anChina
  3. 3.International Joint Research Center for Wireless Communication and Information ProcessingXi’anChina

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