  In recent years, Siamese network based trackers have significantly advanced
the state-of-the-art in real-time tracking. Despite their success, Siamese
trackers tend to suffer from high memory costs, which restrict their
applicability to mobile devices with tight memory budgets. To address this
issue, we propose a distilled Siamese tracking framework to learn small, fast
and accurate trackers (students), which capture critical knowledge from large
Siamese trackers (teachers) by a teacher-students knowledge distillation model.
This model is intuitively inspired by the one teacher vs. multiple students
learning method typically employed in schools. In particular, our model
contains a single teacher-student distillation module and a student-student
knowledge sharing mechanism. The former is designed using a tracking-specific
distillation strategy to transfer knowledge from a teacher to students. The
latter is utilized for mutual learning between students to enable in-depth
knowledge understanding. Extensive empirical evaluations on several popular
Siamese trackers demonstrate the generality and effectiveness of our framework.
Moreover, the results on five tracking benchmarks show that the proposed
distilled trackers achieve compression rates of up to 18$\times$ and
frame-rates of $265$ FPS, while obtaining comparable tracking accuracy compared
to base models.
