  In this paper, we investigate the impacts of three main aspects of visual
tracking, i.e., the backbone network, the attentional mechanism, and the
detection component, and propose a Siamese Attentional Keypoint Network, dubbed
SATIN, for efficient tracking and accurate localization. Firstly, a new Siamese
lightweight hourglass network is specially designed for visual tracking. It
takes advantage of the benefits of the repeated bottom-up and top-down
inference to capture more global and local contextual information at multiple
scales. Secondly, a novel cross-attentional module is utilized to leverage both
channel-wise and spatial intermediate attentional information, which can
enhance both discriminative and localization capabilities of feature maps.
Thirdly, a keypoints detection approach is invented to trace any target object
by detecting the top-left corner point, the centroid point, and the
bottom-right corner point of its bounding box. Therefore, our SATIN tracker not
only has a strong capability to learn more effective object representations,
but also is computational and memory storage efficiency, either during the
training or testing stages. To the best of our knowledge, we are the first to
propose this approach. Without bells and whistles, experimental results
demonstrate that our approach achieves state-of-the-art performance on several
recent benchmark datasets, at a speed far exceeding 27 frames per second.
