Particle filter-based modulation domain infrared targets tracking

  • Xiaofang Kong
  • Qian ChenEmail author
  • Guohua Gu
  • Weixian Qian
  • Kan Ren
  • Jonathan Williams


Faced with problems of low contrast, poor SNR, and relatively complicated tracking environment, stable infrared target tracking is worth researching for its many potential applications. In this paper, instead of traditional target tracking in the pixel domain, we propose a sampling importance resampling (SIR) particle filter method with indirect velocity measurements to track infrared targets in the modulation domain. The dominant amplitude modulation (AM) features used for tracking is extracted by decomposing the input image using an 18-channel Gabor filter bank followed by the application of the dominant component analysis approach. The dominant AM modulation features provide a significant partial texture characteristic of the target which can be separated from background with better discrimination. To take advantage of observed kinematics, we utilize the augmented state vector with indirect velocity information via combining the measurements of velocity in adjacent frames to the SIR particle filter framework, which weakens weights of particles with bad velocity estimates but still having association with the cluttered background or other moving objects. A dynamic template update strategy is also provided to prevent the tracker from appearance model drift. Experiments indicate that the proposed method is effective for raising the tracking accuracy compared with other tracking methods.


Infrared target tracking SIR particle filter Modulation domain AM features Dominant component analysis Augmented state vector 



This work was supported by the National Nature Science Foundation of China (Grant No. 61701233), and China Scholarship. The author would also like to thank Professor Joseph Havlicek and Doctor Jonathan Williams of the University of Oklahoma for the great help of the research.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest concerning the content of this study.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Xiaofang Kong
    • 1
  • Qian Chen
    • 1
    Email author
  • Guohua Gu
    • 1
  • Weixian Qian
    • 1
  • Kan Ren
    • 1
  • Jonathan Williams
    • 2
  1. 1.Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, School of Electronic and Optical EngineeringNanjing University of Science and TechnologyNanjingChina
  2. 2.School of Electrical and Computer EngineeringUniversity of OklahomaNormanUSA

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