Correlation Filter Tracking Algorithm Based on Multiple Features and Average Peak Correlation Energy

  • Xiyan Sun
  • Kaidi Zhang
  • Yuanfa JiEmail author
  • Shouhua Wang
  • Suqing Yan
  • Sunyong Wu
Part of the Studies in Computational Intelligence book series (SCI, volume 810)


The traditional target tracking algorithm adopts artificial features. However, the artificial features are not strong enough to illustrate the appearance of the target. So it is difficult to apply to complex scenes; moreover, the traditional target tracking algorithm does not judge the confidence of the response. This paper proposes the Multiple Features and Average Peak Correlation Energy (MFAPCE) tracking algorithm, MFAPCE tracking algorithm combines deep features with color features and uses average peak correlation energy to measure confidence. Finally, according to the confidence to determine whether to update the model. Compared with the traditional tracking algorithm, MFAPCE algorithm can improve the tracking performance according to experiment.


Correlation filter Target tracking Deep features Average peak correlation energy 



This work was supported by the National Natural Science Foundation of China (61561016, 11603041), Guangxi Information Science Experiment Center funded project, Department of Science and Technology of Guangxi Zhuang Autonomous Region (AC16380014, AA17202048, AA17202033).


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Xiyan Sun
    • 1
  • Kaidi Zhang
    • 1
  • Yuanfa Ji
    • 1
    Email author
  • Shouhua Wang
    • 1
  • Suqing Yan
    • 1
  • Sunyong Wu
    • 1
  1. 1.Guangxi Key Laboratory of Precision Navigation Technology and ApplicationGuilin University of Electronic TechnologyGuilinChina

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