L2G-transT: a local-to-global multi-scale attention framework for robust vehicle tracking

Vehicle tracking in complex traffic environments remains a challenging task due to frequent target occlusion, high visual similarity among vehicles, and cluttered backgrounds. These factors often result in tracking drift and target misidentification, undermining the reliability of tracking systems in real-world applications. To address these challenges, this paper presents L2G-TransT, a vehicle tracking framework based on local-to-global multi-scale attention. The proposed method hierarchically integrates fine-grained local features, mid-scale spatiotemporal context, and global semantic cues through a combination of window attention, shifted window attention, and global self-attention mechanisms. A dynamic weighting strategy is employed to adaptively fuse multi-scale features based on the target’s visibility and appearance changes, thereby enhancing the model’s adaptability to challenging scenarios. In addition, a multi-head cross-attention module is introduced to improve feature discriminability by suppressing background noise and emphasizing target-relevant regions. Extensive experiments demonstrate that L2G-TransT achieves better tracking performance, improving both accuracy and robustness under occlusion and appearance ambiguity. The codes and datasets are available at https://github.com/chd-via-lab/L2G-TransT.git.