  Tracking-by-detection is a very popular framework for single object tracking
which attempts to search the target object within a local search window for
each frame. Although such local search mechanism works well on simple videos,
however, it makes the trackers sensitive to extremely challenging scenarios,
such as heavy occlusion and fast motion. In this paper, we propose a novel and
general target-aware attention mechanism (termed TANet) and integrate it with
tracking-by-detection framework to conduct joint local and global search for
robust tracking. Specifically, we extract the features of target object patch
and continuous video frames, then we concatenate and feed them into a decoder
network to generate target-aware global attention maps. More importantly, we
resort to adversarial training for better attention prediction. The appearance
and motion discriminator networks are designed to ensure its consistency in
spatial and temporal views. In the tracking procedure, we integrate the
target-aware attention with multiple trackers by exploring candidate search
regions for robust tracking. Extensive experiments on both short-term and
long-term tracking benchmark datasets all validated the effectiveness of our
algorithm. The project page of this paper can be found at
\url{https://sites.google.com/view/globalattentiontracking/home/extend}.
