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Grayscale-Thermal Tracking via Inverse Sparse Representation based Collaborative Encoding.

Bin Kang, Dong Liang, Wan Ding

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |December 28, 2019
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    Summary
    This summary is machine-generated.

    This study introduces a new joint correlation and discriminant analysis model for grayscale-thermal tracking. It effectively fuses complementary target information, improving tracking accuracy in challenging scenarios.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Sensor Fusion

    Background:

    • Grayscale-thermal tracking fuses complementary visual data for enhanced target observation.
    • Existing methods often treat target discrimination and correlation separately, leading to tracking drift.

    Purpose of the Study:

    • To develop a unified model for joint grayscale-thermal tracking.
    • To address the interdependence of target discrimination and correlation in tracking.

    Main Methods:

    • Introduced a joint correlation and discriminant analysis based inver-sparse representation (JCDA-InvSR) model.
    • Employed multi-objective programming to integrate feature selection and multi-view correlation analysis.
    • Incorporated prior knowledge into an SVM classifier to prevent overfitting.

    Main Results:

    • The JCDA-InvSR model simultaneously optimizes target discrimination and cross-image correlation.
    • Demonstrated improved tracking performance on GTOT and RGBT234 datasets.
    • Achieved robust grayscale-thermal tracking by overcoming limitations of separate analyses.

    Conclusions:

    • The proposed JCDA-InvSR framework offers a significant advancement in grayscale-thermal tracking.
    • Jointly optimizing discrimination and correlation leads to more robust and accurate tracking.
    • The method effectively handles challenging video pairs by leveraging complementary sensor data.