SAMURAI is a visual object tracking model based on the Segment Anything Model 2 (SAM 2), specifically designed to handle the visual tracking task of fast-moving or self-occluding objects. It effectively predicts object motion and optimizes mask selection by introducing temporal motion cues and a motion-aware memory selection mechanism, achieving robust and accurate tracking without the need for retraining or fine-tuning. SAMURAI operates in real-time environments and demonstrates strong zero-shot performance across multiple benchmark datasets, proving its generalization capability without the need for fine-tuning. In evaluations, SAMURAI showed significant improvements in success rate and accuracy compared to existing trackers, with a 7.1% AUC increase on LaSOT-ext and a 3.5% AO increase on GOT-10k. Furthermore, compared to fully supervised methods on LaSOT, SAMURAI demonstrates competitive results, underscoring its robustness in complex tracking scenarios and potential practical value in dynamic environments.