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Deep Learning in Visual Tracking: A Review.

Licheng Jiao, Dan Wang, Yidong Bai

    IEEE Transactions on Neural Networks and Learning Systems
    |December 30, 2021
    PubMed
    Summary

    Deep learning (DL) significantly advances visual tracking by improving feature representation and network architectures. This review covers DL

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Deep learning (DL) has revolutionized computer vision tasks, including visual tracking.
    • DL's influence extends to similarity metrics, data association, and bounding box estimation in tracking.

    Purpose of the Study:

    • To comprehensively review the development and impact of deep learning research in visual tracking.
    • To analyze critical improvements DL has brought to visual tracking methodologies.

    Main Methods:

    • Overview of deep learning advancements in feature representation and network architectures for visual tracking.
    • Analysis of DL applications in addressing key tracking challenges: spatiotemporal integration, target classification, target update, and bounding box estimation.
    • Inclusion of both single-object tracking and multiple-object tracking subtasks in the survey.

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    Main Results:

    • Deep learning-based trackers have achieved state-of-the-art performance in visual tracking.
    • DL has led to critical improvements in feature representation, network design, and core tracking issues.
    • The survey provides an analysis of DL-based approaches' performance.

    Conclusions:

    • Deep learning is integral to modern visual tracking, driving significant performance gains.
    • The review highlights the broad impact of DL across various aspects of visual tracking.
    • Future research directions and tasks in DL-based visual tracking are identified.