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    Hyperspectral imaging (HSI) enhances object tracking by using material information. This study introduces a new HSI dataset and material features for robust tracking in challenging conditions.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Remote Sensing

    Background:

    • Traditional color-based object tracking struggles with background clutter and appearance changes.
    • Hyperspectral images (HSI) offer rich material information, providing robustness to challenging tracking scenarios.

    Purpose of the Study:

    • To investigate the utilization of material information for enhancing object tracking.
    • To develop and evaluate new methods for material-based object tracking using HSI.

    Main Methods:

    • Construction of a novel, fully-annotated dataset with synchronized hyperspectral and color video sequences.
    • Representation of material information using spectral-spatial histograms of multidimensional gradients and fractional abundances.
    • Integration of these material features into correlation filters for material-based tracking.

    Main Results:

    • Demonstrated the potential of material information to improve object tracking performance.
    • Validated the advantages of the proposed material-based tracking approach on the new dataset.

    Conclusions:

    • Material information derived from hyperspectral images significantly boosts object tracking robustness.
    • The developed dataset and feature representations provide a foundation for future research in material-based tracking.