Dynamic Switching State Systems for Visual Tracking

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Focusing on the dynamics of maneuvering objects for visual tracking, this work explores the integration of recursive Bayesian filters with deep learning techniques for state estimation. It presents a comprehensive approach that combines these methodologies to enhance the understanding and effectiveness of visual tracking systems.