Is there a state-of-the-art deep learning paper that uses center point regression instead of bounding box regression, for object tracking?

Almost all deep learning based object tracking methods perform bounding box regression. Siamese-based networks which are very popular for object tracking also perform bounding box regression most of the time, although SiamFC type exceptions exist. And some other networks use a detection + tracking mechanism using the center points of the objects, however, what I am looking for is whether any Siamese based networks that predict the center of the object rather than the bounding box exist. I searched the literature but couldn't find any, I would appreciate any guidance.