Interactive Classification and Regression for Visual Tracking with Dual Update Strategy

The current two-stage tracking method locates the target using the position with the highest confidence score, and updates the template using a carefully designed template update strategy. However, we identify two key issues with these trackers: 1) the update strategy lacks continuous, cost-free template adaptation, leading to suboptimal tracking under appearance changes, and 2) the location with the highest confidence score does not always yield accurate bounding boxes, potentially resulting in incomplete target coverage. In this paper, we propose a novel tracker that incorporates two key innovations. First, the tracker employs a dual update strategy that performs online template updates at both the image and feature levels. This strategy enables continuous adaptation to target appearance changes without introducing additional computational overhead. Second, we enhance the existing loss function by introducing a Classification-Regression Interaction (CRI) loss, which guides the training process to produce confidence scores that more accurately reflect the quality of the predicted bounding boxes. Extensive experiments are conducted to evaluate the performance of our tracker and the effectiveness of the proposed methods. The experimental results show that our method has achieved a comprehensive improvement over the baseline on five datasets, and achieves competitive performance compared to state-of-the-art trackers.