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The receding horizon estimation is applied to design robust visual trackers. Most recent data within the fixed size of windows is receding, and is processed to obtain an estimate of the object state at the current time. In visual tracking such a scheme improves filter accuracy by avoiding accumulated approximation errors. A newly derived unscented Kalman filter (UKF) based on the receding horizon strategy is proposed for determining the importance density of the hybrid particle filter. The importance density derived by the receding horizon-based UKF (RHUKF) provides significantly improved accuracy and performance consistency compared to the unscented particle filter (UPF). Visual tracking examples are subsequently tested to demonstrate the advantages of the filter.