A framework of specialized knowledge distillation for Siamese tracker on challenging attributes

Abstract

In recent years, Siamese network-based trackers have achieved significant improvements in real-time tracking. Despite their success, performance bottlenecks caused by unavoidably complex scenarios in target-tracking tasks are becoming increasingly non-negligible. For example, occlusion and fast motion are factors that can easily cause tracking failures and are labeled in many high-quality tracking databases as challenging attributes. In addition, Siamese trackers tend to suffer from high memory costs, which restricts their applicability to mobile devices with tight memory budgets. To address these issues, we propose a Specialized teachers Distilled Siamese Tracker (SDST) framework to learn a student tracker, which is small, fast, and has enhanced performance in challenging attributes. SDST introduces two types of teachers for multi-teacher distillation: general teacher and specialized teachers. The former imparts basic knowledge to the students. The latter is used to transfer specialized knowledge to students, which helps improve their performance in challenging attributes. For students to efficiently capture critical knowledge from the two types of teachers, SDST is equipped with a carefully designed multi-teacher knowledge distillation model. Our model contains two processes: general teacher-student knowledge transfer and specialized teachers-student knowledge transfer. Extensive empirical evaluations of several popular Siamese trackers demonstrated the generality and effectiveness of our framework. Moreover, the results on Large-scale Single Object Tracking (LaSOT) show that the proposed method achieves a significant improvement of more than 2–4% in most challenging attributes. SDST also maintained high overall performance while achieving compression rates of up to 8x and framerates of 252 FPS and obtaining outstanding accuracy on all challenging attributes.