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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.

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I know that some bounding box models like yolo has output of 4 channels: hight-center, width-center,hight-size, width. and with they constract the bounding box, so you can modify those models to use only the first 2 channels.

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