  Recently, deep learning has achieved very promising results in visual object
tracking. Deep neural networks in existing tracking methods require a lot of
training data to learn a large number of parameters. However, training data is
not sufficient for visual object tracking as annotations of a target object are
only available in the first frame of a test sequence. In this paper, we propose
to learn hierarchical features for visual object tracking by using tree
structure based Recursive Neural Networks (RNN), which have fewer parameters
than other deep neural networks, e.g. Convolutional Neural Networks (CNN).
First, we learn RNN parameters to discriminate between the target object and
background in the first frame of a test sequence. Tree structure over local
patches of an exemplar region is randomly generated by using a bottom-up greedy
search strategy. Given the learned RNN parameters, we create two dictionaries
regarding target regions and corresponding local patches based on the learned
hierarchical features from both top and leaf nodes of multiple random trees. In
each of the subsequent frames, we conduct sparse dictionary coding on all
candidates to select the best candidate as the new target location. In
addition, we online update two dictionaries to handle appearance changes of
target objects. Experimental results demonstrate that our feature learning
algorithm can significantly improve tracking performance on benchmark datasets.
