  The task object tracking is vital in numerous applications such as autonomous
driving, intelligent surveillance, robotics, etc. This task entails the
assigning of a bounding box to an object in a video stream, given only the
bounding box for that object on the first frame. In 2015, a new type of video
object tracking (VOT) dataset was created that introduced rotated bounding
boxes as an extension of axis-aligned ones. In this work, we introduce a novel
end-to-end deep learning method based on the Transformer Multi-Head Attention
architecture. We also present a new type of loss function, which takes into
account the bounding box overlap and orientation.
  Our Deep Object Tracking model with Circular Loss Function (DOTCL) shows an
considerable improvement in terms of robustness over current state-of-the-art
end-to-end deep learning models. It also outperforms state-of-the-art object
tracking methods on VOT2018 dataset in terms of expected average overlap (EAO)
metric.
