Fast compressive tracking combined with Kalman filter

  • Jinguang Chen
  • Xiaoxing Li
  • Mingming Wang
  • Lili Ma
  • Bugao XuEmail author


Compressive tracking refers to a group of high-speed algorithms for real-time object tracking. Many tracking algorithms may not generate accurate tracking results because they used fixed learning rates, and sometime lose targets when objects are occluded or deformed. To address these problems, a fast tracking algorithm combined with Kalman filter was proposed in this research. Firstly, an object location was initialized by the predicted value of Kalman filter when it was occluded, and the Kalman update was implemented only when the object was detected. The object location obtained in the Kalman update stage was used later as the initial position in the next frame. Secondly, when the distribution of positive samples satisfied a threshold, an adaptive learning rate was then updated. Finally, the naive Bayes classifier was updated with samples which had more different features. In the experiment, the proposed algorithm was compared with other state-of-the-art algorithms on seven publicly tested sequences, demonstrating that it had higher tracking accuracy and robustness in conditions such as occlusion, deformation and rotation.


Compressive tracking Kalman filter Secondary localization Object tracking 



This work was supported by National Natural Science Foundation of China (61601358), the Natural Science Basic Research Plan in Shaanxi Province of China (2016JM6030), the Scientific Research Program funded by Shaanxi Provincial Education Department (18JK0349).

Compliance with ethical standards

Conflicts of interest

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Jinguang Chen
    • 1
  • Xiaoxing Li
    • 1
  • Mingming Wang
    • 1
  • Lili Ma
    • 1
  • Bugao Xu
    • 1
    • 2
    Email author
  1. 1.School of Computer ScienceXi’an Polytechnic UniversityXi’anChina
  2. 2.Department of Computer Science and EngineeringUniversity of North TexasDentonUSA

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