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    This study presents a new likelihood model for object tracking in multi-view systems. The model transforms Euclidean estimation to a manifold tangent subspace, improving tracking accuracy and performance.

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    Area of Science:

    • Computer Vision
    • Robotics
    • Machine Learning

    Background:

    • Object tracking in multi-view systems is crucial for various applications.
    • Conventional nonlinear Euclidean estimation models face challenges with complex data.
    • A need exists for more robust and accurate tracking methodologies.

    Purpose of the Study:

    • To introduce a novel likelihood model for object tracking in multiple view systems.
    • To transform conventional nonlinear Euclidean estimation into a manifold tangent subspace model.
    • To develop efficient maximum likelihood estimation approaches for improved tracking.

    Main Methods:

    • Decomposition of input noise into two parts.
    • Description of the model using an exponential map.
    • Transformation of Euclidean observations to the manifold tangent subspace.
    • Derivation of a tangent subspace likelihood function.

    Main Results:

    • Numerical results demonstrate the good performance of the proposed model.
    • The model effectively transforms real observations to the manifold tangent subspace.
    • Two maximum likelihood estimation approaches (iterative and noniterative) were proposed and validated.

    Conclusions:

    • The proposed likelihood model offers a promising approach for object tracking in multi-view systems.
    • The manifold tangent subspace framework enhances estimation accuracy.
    • The developed iterative and noniterative methods provide effective solutions for maximum likelihood estimation.