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Correlation Filter Tracking Algorithm Based on Spatio-Temporal Context

  • Jin DieEmail author
  • Na Li
  • Ying Liu
  • Yangyang Wu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1074)

Abstract

Object tracking has been widely used in artificial intelligence, military reconnaissance, security monitoring and other fields. It has become a research hotspot of computer vision. To handle the drift problem in the presence of occlusions, a tracker combined with spatio-temporal context information and correlation filter is proposed in this paper. HOG (Histogram of Oriented Gradient), CN (Color Name) and gray features are extracted to learn the correlation filter. Meanwhile, the spatio-temporal context model is trained. The response map of correlation filter and spatio-temporal context model are normalized and fused. Experimental results show that the proposed algorithm can accurately track the object, and has better performance in terms of successful rate, center position error and distance precision.

Keywords

Object tracking Spatio-temporal context Correlation filter 

Notes

Acknowledgments

This work is supported by the graduate innovation fund project of Xi’an university of posts and telecommunications. (Grant No. CXJJLY2018027). Shaanxi Science and Technology Innovation and Entrepreneurship Dual Tutor System Project (2019JM-604), National Science Foundation of China (61601362, 61571361).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Communication and Information EngineeringXi’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 Processing TechnologyXi’anChina

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