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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Infrared Imaging

    Background:

    • Kernel Correlation Filter (KCF) tracking accuracy degrades with significant scale and rotation variations in aerial infrared targets.
    • Existing methods struggle to accurately estimate target scale and orientation under dynamic conditions.

    Purpose of the Study:

    • To propose a novel scale estimation KCF-based method for aerial infrared target tracking.
    • To enhance tracking accuracy by effectively extracting and utilizing scale and rotation information from frequency-domain features.

    Main Methods:

    • Utilizes KCF for initial target localization.
    • Extracts scale features from frequency-domain energy distribution and change laws.
    • Employs spectral eigenvalues and frequency-domain rotation scale invariance to determine target rotation.
    • Applies reverse rotation to isolate rotation effects on scale estimation.
    • Estimates target scale using eigenvectors between adjacent frames.
    • Updates tracking box scale and aspect ratio based on rotation information.

    Main Results:

    • The proposed algorithm demonstrates suitability for tracking targets with stable scales and rapid attitude changes.
    • Achieved an average tracking accuracy of 0.954 and an average success rate of 0.782.
    • Outperformed the standard KCF algorithm by 5.3% in accuracy and 18.9% in success rate.
    • Improved average tracking success rate by 4.1% compared to the discriminative scale space tracker.
    • Exhibited superior performance over other related filter tracking algorithms using different scale estimation methods.

    Conclusions:

    • The developed method significantly improves the adaptability of the tracking box to target scale and rotation variations.
    • The frequency-domain approach effectively enhances target scale information estimation.
    • The algorithm provides a robust solution for aerial infrared target tracking in challenging scenarios.