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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Extending Correlation Filter-Based Visual Tracking by Tree-Structured Ensemble and Spatial Windowing.

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    This study introduces a novel ensemble of correlation filter-based (CFB) trackers to improve visual tracking. By registering multiple CFB trackers and using spatial windowing, the algorithm enhances robustness to appearance changes.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Robotics

    Background:

    • Correlation filter-based (CFB) trackers offer efficiency in visual tracking.
    • Current CFB methods often fail due to discarding past target poses and lack of adaptability to appearance changes.

    Purpose of the Study:

    • To develop an advanced visual tracking algorithm that overcomes limitations of single-filter CFB methods.
    • To improve tracking robustness by leveraging historical target appearance information.

    Main Methods:

    • A novel tracking algorithm utilizing a large ensemble of CFB trackers organized in a binary tree structure.
    • Each tracker in the ensemble specializes in a specific target appearance subspace.
    • A spatial windowing technique is introduced to enhance individual expert trackers and improve correlation robustness.

    Main Results:

    • The proposed ensemble approach effectively registers and utilizes knowledge from previous target poses.
    • The algorithm demonstrates boosted tracking decisions by combining relevant appearance-aware experts.
    • Extensive experiments on benchmark datasets show a substantial performance increase.

    Conclusions:

    • The novel ensemble CFB tracking algorithm with spatial windowing significantly enhances visual tracking performance.
    • This approach offers a more robust solution for tracking targets with changing appearances.