  The tracking-by-detection framework requires a set of positive and negative
training samples to learn robust tracking models for precise localization of
target objects. However, existing tracking models mostly treat different
samples independently while ignores the relationship information among them. In
this paper, we propose a novel structure-aware deep neural network to overcome
such limitations. In particular, we construct a graph to represent the pairwise
relationships among training samples, and additionally take the natural
language as the supervised information to learn both feature representations
and classifiers robustly. To refine the states of the target and re-track the
target when it is back to view from heavy occlusion and out of view, we
elaborately design a novel subnetwork to learn the target-driven visual
attentions from the guidance of both visual and natural language cues.
Extensive experiments on five tracking benchmark datasets validated the
effectiveness of our proposed method.
