Target-Driven and Student-Centered Knowledge Distillation for Traffic Object Tracking

Visual Object Tracking is crucial for autonomous driving, enabling real-time monitoring of dynamic environments. While Transformer-based trackers achieve state-of-the-art performance by modeling long-range dependencies, their high computational cost limits deployment in real-world autonomous systems. To address this, we propose Target-Driven and Student-Centered Knowledge Distillation (TDSC-KD), a novel framework designed to improve the efficiency of Transformer-based trackers while maintaining accuracy. Our framework consists of (1) target-driven distillation, which leverages a ground-truth query to guide knowledge transfer toward relevant and consistent regions, filtering out background noise, and (2) student-centered distillation, which employs a mask-and-reconstruct mechanism to encourage more active student learning and reduce over-reliance on the teacher. Experiments on the LaSOT-Traffic dataset demonstrate our TDSC-KD's efficacy, narrowing the gap between the strong performance of Transformer trackers and the strict efficiency constraints of real-world deployment.