CVT-Track: Concentrating on Valid Tokens for One-Stream Tracking

Abstract

In the domain of single object tracking, the Ground Truth bounding box is intentionally sized larger than the minimum dimensions required to enclose the target in the initial video frame, inadvertently including extraneous elements and interferences in the template image. Moreover, significant appearance changes of the target during movement present substantial challenges for maintaining robust tracking. To address these issues, this study introduces a novel one-stream tracking framework named CVT-Track. CVT-Track comprises two main components: the Target Valid Token Collection (TaVTC) and the Temporal Valid Token Collection (TeVTC) modules. The TaVTC module effectively mitigates background noise and interference from similar targets, thereby sharpening the focus on the target’s unique features and enhancing tracking accuracy. Conversely, the TeVTC module skillfully extracts target information from historical frames, capturing the target’s dynamic appearance changes throughout the tracking process and thereby improving tracking robustness. The synergistic operation of these modules markedly enhances both the accuracy and robustness of tracking. Empirical evaluations demonstrate that CVT-Track achieves state-of-the-art performance across multiple datasets and maintains superior inference speeds.