Dynamic Switching State Systems for Visual Tracking

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.