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Noise-Aware Framework for Robust Visual Tracking.

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    A novel noise-aware (NA) framework enhances visual tracking by adaptively suppressing noise. This approach improves signal-to-noise ratio (SNR) in region of interest (ROI) analysis, boosting tracker performance and robustness against variations.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Robotics

    Background:

    • Siamese networks and correlation filters (CF) excel at visual tracking by measuring target similarity.
    • Fixed window functions in these trackers are susceptible to noise, leading to model drift.
    • Low signal-to-noise ratio (SNR) in windowed regions of interest (ROIs) degrades tracker performance.

    Purpose of the Study:

    • To introduce a noise-aware (NA) framework for robust visual tracking.
    • To investigate the impact of window functions on tracker performance under noisy conditions.
    • To enhance the SNR of windowed ROIs for improved tracking accuracy and scale estimation.

    Main Methods:

    • Investigated the effect of various window functions on visual tracking performance.
    • Developed a novel NA window that adaptively suppresses noise based on similarity map observations.
    • Utilized a particle filter to dynamically sample windowed ROIs for optimized scale estimation.

    Main Results:

    • The NA framework significantly improves the SNR of windowed ROIs, mitigating tracker degeneration.
    • The proposed NA window effectively suppresses variable noise, enhancing tracking robustness.
    • Experimental results across multiple datasets demonstrate superior performance of NA framework variants over baseline trackers.

    Conclusions:

    • The developed NA framework is a versatile enhancement applicable to various Siamese and CF trackers.
    • The NA framework achieves robust visual tracking with only a minor impact on computational efficiency.
    • Adaptive noise suppression and optimized ROI sampling are key to improving tracker performance in challenging conditions.