  Existing deep trackers mainly use convolutional neural networks pre-trained
for generic object recognition task for representations. Despite demonstrated
successes for numerous vision tasks, the contributions of using pre-trained
deep features for visual tracking are not as significant as that for object
recognition. The key issue is that in visual tracking the targets of interest
can be arbitrary object class with arbitrary forms. As such, pre-trained deep
features are less effective in modeling these targets of arbitrary forms for
distinguishing them from the background. In this paper, we propose a novel
scheme to learn target-aware features, which can better recognize the targets
undergoing significant appearance variations than pre-trained deep features. To
this end, we develop a regression loss and a ranking loss to guide the
generation of target-active and scale-sensitive features. We identify the
importance of each convolutional filter according to the back-propagated
gradients and select the target-aware features based on activations for
representing the targets. The target-aware features are integrated with a
Siamese matching network for visual tracking. Extensive experimental results
show that the proposed algorithm performs favorably against the
state-of-the-art methods in terms of accuracy and speed.
