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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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Visual Object Tracking by Hierarchical Attention Siamese Network.

Jianbing Shen, Xin Tang, Xingping Dong

    IEEE Transactions on Cybernetics
    |September 20, 2019
    PubMed
    Summary
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    This study introduces a novel visual tracking method using a hierarchical attention Siamese network. The approach enhances target localization accuracy by integrating attention mechanisms and multilevel features, outperforming existing trackers.

    Area of Science:

    • Computer Vision
    • Artificial Intelligence

    Background:

    • Visual tracking aims to precisely locate targets in videos.
    • Existing Siamese networks can struggle with complex tracking scenarios.

    Purpose of the Study:

    • To develop an advanced visual tracking method using attention mechanisms and multilevel features.
    • To improve matching discrimination and tracking accuracy.

    Main Methods:

    • Introduced an attention mechanism into a Siamese network using a sub-Siamese network (Attention Net).
    • Utilized multilevel features (semantic and location) and fused multiscale response maps.
    • Proposed a hierarchical attention Siamese network combining attention and multilayer integration.

    Main Results:

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  • The proposed method achieved superior performance compared to state-of-the-art Siamese trackers.
  • Effective even without fine-tuning or online updating, leveraging a pretrained network.
  • Demonstrated improved accuracy in object position estimation.

    • Conclusions:

    • The hierarchical attention Siamese network effectively enhances visual tracking performance.
    • The novel attention computation and feature fusion strategies improve robustness and accuracy.
    • The method offers a competitive solution for visual object tracking challenges.