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Learning dual-margin model for visual tracking.

Nana Fan1, Xin Li1, Zikun Zhou1

  • 1School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China.

Neural Networks : the Official Journal of the International Neural Network Society
|April 30, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a dual-margin model for robust visual tracking, effectively distinguishing targets from backgrounds and handling appearance changes. The method achieves real-time, accurate tracking across multiple datasets.

Keywords:
Dual marginSiamese networkVisual tracking

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Area of Science:

  • Computer Vision
  • Machine Learning

Background:

  • Visual tracking faces challenges with target appearance variations.
  • Existing methods struggle with limited spatial information and overfitting in online updates.

Purpose of the Study:

  • To propose a dual-margin model for robust and accurate visual tracking.
  • To address limitations of current tracking techniques by incorporating both target-background discrimination and intra-target appearance changes.

Main Methods:

  • Developed a dual-margin model with intra-object and inter-object margins.
  • Trained the model offline on a large video dataset to learn appearance variations.
  • Implemented tracking within a Siamese framework using appearance sets as templates.

Main Results:

  • Achieved accurate and robust tracking performance on five public datasets.
  • Demonstrated real-time processing capabilities.
  • Outperformed state-of-the-art methods in visual tracking benchmarks.

Conclusions:

  • The dual-margin model effectively handles target appearance changes and background clutter.
  • The proposed algorithm offers a significant advancement in robust and accurate visual tracking.
  • The method's effectiveness is validated by its superior performance on benchmark datasets.