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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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SiamOT: An Improved Siamese Network with Online Training for Visual Tracking.

Xiaomei Gong1, Yuxin Zhou1, Yi Zhang1

  • 1Department of Computer Science, Sichuan University, Chengdu 610017, China.

Sensors (Basel, Switzerland)
|September 9, 2022
PubMed
Summary

This study introduces SiamOT, a novel twin-branch Siamese network for visual tracking. SiamOT enhances tracking accuracy by integrating online learning to capture both target and background information, improving performance on unknown targets.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence

Background:

  • Siamese networks are effective for visual tracking using convolutional neural networks and weight-sharing.
  • Offline training strategies in current Siamese networks struggle with unknown targets due to inability to learn background information, leading to performance degradation and tracking failures.

Purpose of the Study:

  • To address the limitations of existing Siamese networks in handling unknown targets and background interference.
  • To propose a novel twin-branch architecture, SiamOT, for robust visual tracking.

Main Methods:

  • Developed a twin-branch architecture (SiamOT) comprising a classical Siamese network and an online training branch.
  • The online branch incorporates feature fusion and an attention mechanism to capture and update both target and background information.
Keywords:
Siamese networksonline trainingvisual tracking

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  • Refined target descriptions by integrating online-learned background context.

    • Main Results:

    • SiamOT demonstrated superior performance on three mainstream benchmarks.
    • The proposed architecture showed stronger target discrimination abilities compared to existing methods.
    • Ablation studies validated the effectiveness of the individual components of SiamOT.

    Conclusions:

    • SiamOT effectively mitigates performance degradation in visual tracking caused by unknown targets and background interference.
    • The integration of an online training branch with feature fusion and attention mechanisms enhances target description and tracking robustness.
    • SiamOT represents a significant advancement in Siamese network-based visual tracking, offering improved accuracy and reliability.