  Siamese network based trackers develop rapidly in the field of visual object
tracking in recent years. The majority of siamese network based trackers now in
use treat each channel in the feature maps generated by the backbone network
equally, making the similarity response map sensitive to background influence
and hence challenging to focus on the target region. Additionally, there are no
structural links between the classification and regression branches in these
trackers, and the two branches are optimized separately during training.
Therefore, there is a misalignment between the classification and regression
branches, which results in less accurate tracking results. In this paper, a
Target Highlight Module is proposed to help the generated similarity response
maps to be more focused on the target region. To reduce the misalignment and
produce more precise tracking results, we propose a corrective loss to train
the model. The two branches of the model are jointly tuned with the use of
corrective loss to produce more reliable prediction results. Experiments on 5
challenging benchmark datasets reveal that the method outperforms current
models in terms of performance, and runs at 38 fps, proving its effectiveness
and efficiency.
