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Improved infrared target-tracking algorithm based on mean shift.

Zhile Wang1, Qingyu Hou, Ling Hao

  • 1Research Center for Space Optical Engineering, Harbin Institute of Technology, Harbin, China. wangzhile_hit@163.com

Applied Optics
|August 4, 2012
PubMed
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This study introduces an improved infrared (IR) target-tracking algorithm using mean shift. The enhanced method refines target representation and effectively suppresses background interference for better tracking accuracy.

Area of Science:

  • Computer Vision
  • Signal Processing
  • Artificial Intelligence

Background:

  • Infrared (IR) target tracking is crucial for various applications.
  • Existing algorithms face challenges with background clutter and target appearance variations.
  • Mean shift algorithms offer a robust framework for object tracking.

Purpose of the Study:

  • To develop an improved IR target-tracking algorithm.
  • To enhance the target representation model for better accuracy.
  • To suppress background disturbance in tracking.

Main Methods:

  • A mean-shift-based gradient-matched searching strategy was combined with feature-classification-based tracking.
  • An improved target representation model was constructed using gray-level features and a likelihood ratio.

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  • The mean-shift vector expression and model update criterion were derived.

    • Main Results:

    • The algorithm demonstrated improved shift weight for target pixels.
    • Suppression of background disturbance was observed.
    • Experimental results validated the effectiveness of the proposed algorithm.

    Conclusions:

    • The proposed IR target-tracking algorithm offers enhanced performance.
    • The improved target representation model effectively handles background variations.
    • This method provides a more robust solution for IR target tracking.