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Robust kernel-based tracking with multiple subtemplates in vision guidance system.

Yuzhuang Yan1, Xinsheng Huang, Wanying Xu

  • 1College of Mechatronics Engineering and Automation, National University of Defense Technology, Changsha 410073, China. yuzhuangyan@nudt.edu.cn

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Summary
This summary is machine-generated.

This study introduces a new multiple subtemplates tracking algorithm to improve target tracking precision. The method enhances accuracy for scale-variant targets in gray-level sequences, outperforming the original mean shift algorithm.

Keywords:
mean shiftsubtemplatetrackingvision guidancevoting

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Area of Science:

  • Computer Vision
  • Robotics
  • Machine Learning

Background:

  • The mean shift algorithm is widely used for target tracking due to its simplicity and robustness.
  • However, it struggles with tracking precision, especially for scale-variant targets in grayscale images, due to a lack of spatial information.

Purpose of the Study:

  • To develop a novel multiple subtemplates based tracking algorithm for enhanced target tracking precision in terminal guidance applications.
  • To address limitations of the mean shift algorithm in handling complex scenarios like rotation, scaling, and partial target occlusion.

Main Methods:

  • A novel algorithm utilizing multiple subtemplates for target tracking.
  • An optimal subtemplate selection algorithm to maximize information representation and minimize redundancy.
  • A Gaussian weighted voting method integrating serial tracking results and spatial constraints to determine the target center.
  • Scale factor determination by maximizing voting results across scale searching layers.

Main Results:

  • The proposed multiple subtemplates tracking algorithm significantly improves tracking accuracy compared to the original mean shift algorithm.
  • Experiments on static scene videos demonstrate enhanced performance in handling complex target variations.

Conclusions:

  • The novel multiple subtemplates tracking algorithm offers a robust solution for precise target tracking, particularly in challenging conditions.
  • This approach effectively overcomes the spatial information limitations of traditional methods like mean shift, improving accuracy and handling scale variation.