Recently, a research team from the University of Washington released a new visual tracking model called SAMURAI. This model is based on the Segment Anything Model2 (SAM2) and aims to address the challenges encountered during visual object tracking in complex scenes, particularly when dealing with fast-moving and self-occluding objects. While SAM2 excels in object segmentation tasks, it has some limitations in visual tracking. For instance, its fixed-window memory approach fails to consider objects in crowded scenes.