A cascaded border-aware network for visual tracking

Visual object tracking represents a fundamental challenge within the domain of computer vision. Despite extensive research aimed at enhancing tracking accuracy, the intrinsic complexity of varied scenarios continues to present significant obstacles to achieving robust object tracking performance. In this work, we propose a new Transformer-based tracking framework named CasBAN (cascaded border-aware network) to explore effective approaches for improving tracking accuracy. Our framework is built upon a traditional vision Transformer backbone, augmented by a corner prediction tracking head. Within this tracking head, we implement a historical prompt computation mechanism to leverage past information effectively, alongside a border-aware network that extracts direct border features of the object’s bounding box to further enhance tracking accuracy. Additionally, a cascade tracking strategy is adopted for refined bounding box regression. Experiments on six publicly available datasets demonstrate the effectiveness of our method.