  Siamese Networks are one of most popular visual object tracking methods for
their high speed and high accuracy tracking ability as long as the target is
well identified. However, most Siamese Network based trackers use the first
frame as the ground truth of an object and fail when target appearance changes
significantly in next frames. They also have dif iculty distinguishing the
target from similar other objects in the frame. We propose two ideas to solve
both problems. The first idea is using a bag of dynamic templates, containing
diverse, similar, and recent target features and continuously updating it with
diverse target appearances. The other idea is to let a network learn the path
history and project a potential future target location in a next frame. This
tracker achieves state-of-the-art performance on the long-term tracking dataset
UAV20L by improving the success rate by a large margin of 15% (65.4 vs 56.6)
compared to the state-of-the-art method, HiFT. The of icial python code of this
paper is publicly available.
