Transformer-augmented dual-branch siamese tracker with confidence-aware regression and adaptive template updating

Visual object tracking using Siamese networks has proven effective by matching a reference target with candidate regions. However, their performance is limited by static templates, insufficient context modeling, and weak multi-level feature integration, especially under occlusion, background clutter, and appearance variation. To address these limitations, we propose TSDTrack, a transformer-augmented Siamese tracker designed for quality-aware and robust tracking. Our framework employs a ResNet backbone to extract multi-scale hierarchical features, which are fused using a transformer-based module that applies global attention to enhance semantic and spatial consistency. The prediction head consists of two branches: a confidence aware branch (CAB) that assesses the confidence of classification responses, and a regression distribution learning (RDL) branch that models bounding box localization as discrete probability distributions, improving precision under uncertainty. Furthermore, we introduce a confidence-gated template update strategy that selectively refreshes the target representation based on the CAB score, enabling adaptive appearance modeling while avoiding drift. Experiments on LaSOT, GOT-10k, OTB100, and UAV123 demonstrate that TSDTrack achieves state-of-the-art performance in both accuracy and robustness, achieving 55.5% success on LaSOT, 67.5% AO on GOT-10k, 71.6% AUC on OTB100, and 66.4% success on UAV123, outperforming recent transformer-based and Siamese trackers.