  In this paper, we propose a novel on-line visual tracking framework based on
the Siamese matching network and meta-learner network, which run at real-time
speeds. Conventional deep convolutional feature-based discriminative visual
tracking algorithms require continuous re-training of classifiers or
correlation filters, which involve solving complex optimization tasks to adapt
to the new appearance of a target object. To alleviate this complex process,
our proposed algorithm incorporates and utilizes a meta-learner network to
provide the matching network with new appearance information of the target
objects by adding target-aware feature space. The parameters for the
target-specific feature space are provided instantly from a single forward-pass
of the meta-learner network. By eliminating the necessity of continuously
solving complex optimization tasks in the course of tracking, experimental
results demonstrate that our algorithm performs at a real-time speed while
maintaining competitive performance among other state-of-the-art tracking
algorithms.
