  In this paper, we present a novel siamese motion-aware network (SiamMan) for
visual tracking, which consists of the siamese feature extraction subnetwork,
followed by the classification, regression, and localization branches in
parallel. The classification branch is used to distinguish the foreground from
background, and the regression branch is adopt to regress the bounding box of
target. To reduce the impact of manually designed anchor boxes to adapt to
different target motion patterns, we design the localization branch, which aims
to coarsely localize the target to help the regression branch to generate
accurate results. Meanwhile, we introduce the global context module into the
localization branch to capture long-range dependency for more robustness in
large displacement of target. In addition, we design a multi-scale learnable
attention module to guide these three branches to exploit discriminative
features for better performance. The whole network is trained offline in an
end-to-end fashion with large-scale image pairs using the standard SGD
algorithm with back-propagation. Extensive experiments on five challenging
benchmarks, i.e., VOT2016, VOT2018, OTB100, UAV123 and LTB35, demonstrate that
SiamMan achieves leading accuracy with high efficiency. Code can be found at
https://isrc.iscas.ac.cn/gitlab/research/siamman.
