Publication Type

Journal Article

Version

acceptedVersion

Publication Date

12-2022

Abstract

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 versus 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× and frame-rates of 265 FPS, while obtaining comparable tracking accuracy compared to base models.

Keywords

Siamese network, Teacher-students, Knowledge distillation, Siamese trackers

Discipline

Artificial Intelligence and Robotics | Theory and Algorithms

Publication

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volume

44

Issue

12

First Page

8896

Last Page

8909

ISSN

0162-8828

Identifier

10.1109/TPAMI.2021.3127492

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Author-CC-BY

Creative Commons License

Creative Commons Attribution 3.0 License
This work is licensed under a Creative Commons Attribution 3.0 License.

Additional URL

https://doi.org/10.1109/TPAMI.2021.3127492

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