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Structure-Regularized Compressive Tracking With Online Data-Driven Sampling.

Qing Guo, Wei Feng, Ce Zhou

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    This study enhances object tracking by improving compressive random projection with structural regularization and data-driven sampling. The new methods yield more discriminative features and accurate localization for robust real-world tracking.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Image Processing

    Background:

    • Compressive random projection is an effective appearance model for object tracking, deriving Haar-like features from non-rotated rectangles.
    • Existing fast compressive tracking schemes can be further improved to enhance object localization accuracy and tracking robustness.

    Purpose of the Study:

    • To significantly improve fast compressive tracking using structural regularization and online data-driven sampling.
    • To introduce superpixel-guided compressive projection for more discriminative feature generation.
    • To enable low-cost extraction of Haar-like features from rotated rectangles for accurate object localization.

    Main Methods:

    • Superpixel-guided compressive projection to capture rich local structural information.
    • Fast directional integration for efficient extraction of Haar-like features from rotated rectangles.
    • Two online data-driven sampling strategies for generating effective candidate and training samples.

    Main Results:

    • Superpixel-guided projection generates more discriminative features.
    • Fast directional integration allows accurate object localization using rotated rectangles.
    • Data-driven sampling strategies produce fewer but more effective samples for detection and classifier updating.
    • The proposed approach demonstrates superior object localization ability and robustness on benchmark datasets.

    Conclusions:

    • The integration of structural regularization and online data-driven sampling significantly enhances compressive tracking performance.
    • The proposed methods offer improved feature discriminability, localization accuracy, and tracking robustness compared to state-of-the-art trackers.