  In this paper, we propose an approach to learn hierarchical features for
visual object tracking. First, we offline learn features robust to diverse
motion patterns from auxiliary video sequences. The hierarchical features are
learned via a two-layer convolutional neural network. Embedding the temporal
slowness constraint in the stacked architecture makes the learned features
robust to complicated motion transformations, which is important for visual
object tracking. Then, given a target video sequence, we propose a domain
adaptation module to online adapt the pre-learned features according to the
specific target object. The adaptation is conducted in both layers of the deep
feature learning module so as to include appearance information of the specific
target object. As a result, the learned hierarchical features can be robust to
both complicated motion transformations and appearance changes of target
objects. We integrate our feature learning algorithm into three tracking
methods. Experimental results demonstrate that significant improvement can be
achieved using our learned hierarchical features, especially on video sequences
with complicated motion transformations.
