  The problem of visual object tracking has traditionally been handled by
variant tracking paradigms, either learning a model of the object's appearance
exclusively online or matching the object with the target in an offline-trained
embedding space. Despite the recent success, each method agonizes over its
intrinsic constraint. The online-only approaches suffer from a lack of
generalization of the model they learn thus are inferior in target regression,
while the offline-only approaches (e.g., convolutional siamese trackers) lack
the target-specific context information thus are not discriminative enough to
handle distractors, and robust enough to deformation. Therefore, we propose an
online module with an attention mechanism for offline siamese networks to
extract target-specific features under L2 error. We further propose a filter
update strategy adaptive to treacherous background noises for discriminative
learning, and a template update strategy to handle large target deformations
for robust learning. Effectiveness can be validated in the consistent
improvement over three siamese baselines: SiamFC, SiamRPN++, and SiamMask.
Beyond that, our model based on SiamRPN++ obtains the best results over six
popular tracking benchmarks and can operate beyond real-time.
