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Robust Object Tracking With Discrete Graph-Based Multiple Experts.

Jiatong Li, Chenwei Deng, Richard Yi Da Xu

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |March 31, 2017
    PubMed
    Summary
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    This study introduces a multi-expert framework to correct visual object tracking drift caused by illumination changes and occlusions. By analyzing historical tracker data, the system effectively improves tracking accuracy and robustness.

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Visual object tracking is challenged by variations in illumination, occlusions, and target deformations, leading to tracking drift.
    • Existing methods often struggle to maintain accurate object state estimation under dynamic and adverse conditions.

    Purpose of the Study:

    • To develop a novel framework for effectively correcting visual object tracking drift.
    • To leverage historical tracker information to enhance the robustness and accuracy of object tracking.

    Main Methods:

    • A multi-expert framework is proposed, integrating the current tracker with historical tracker snapshots.
    • The scheme is formulated as a unified discrete graph optimization problem, enabling exact object state estimation.
    • The framework corrects drift by selecting the optimal expert hypothesis based on graph scores, implicitly analyzing recent tracker performance.

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    Main Results:

    • Integration with an online SVM on a budget algorithm showed significant improvement in tracking performance.
    • Further enhancements were achieved by incorporating regression correlation filters with both hand-crafted and deep convolutional neural network features.
    • Extensive evaluations on TB-50, TB-100, and VOT2015 datasets demonstrated superior performance compared to state-of-the-art methods.

    Conclusions:

    • The proposed multi-expert graph optimization framework effectively corrects visual object tracking drift.
    • The method offers a robust solution for object tracking under challenging conditions, outperforming existing approaches.