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Toward Occlusion Handling in Visual Tracking via Probabilistic Finite State Machines.

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    This study introduces a new visual tracking framework using correlation filters and probabilistic finite state machines (FSMs) to effectively handle target occlusion. The novel approach models target states based on part occlusion, improving tracking robustness and performance against state-of-the-art methods.

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    Area of Science:

    • Computer Vision
    • Machine Learning

    Background:

    • Visual tracking is a long-standing research area in computer vision.
    • Existing tracking methods struggle with challenges like target occlusion and appearance changes.

    Purpose of the Study:

    • To propose a novel framework for robust visual tracking, specifically addressing occlusion.
    • To improve tracking performance by integrating correlation filtering with probabilistic finite state machines.

    Main Methods:

    • The proposed framework partitions the target into parts and automatically detects their occlusion states.
    • Probabilistic finite state machines (FSMs) model target state transitions to mitigate noise from correlation filter responses.
    • An adaptive online update strategy is employed based on the estimated target state.

    Main Results:

    • The framework demonstrates competitive performance against state-of-the-art visual tracking algorithms.
    • Experiments on public benchmarks validate the effectiveness of the proposed approach in handling occlusion.

    Conclusions:

    • The novel framework effectively handles target occlusion in visual tracking.
    • The integration of correlation filtering and probabilistic FSMs offers a robust solution for challenging tracking scenarios.