Progressive Alignment of Multi-Modal Trajectories Under Modality Imbalance: A Case Study in Metro Stations

In dense crowds and complex electromagnetic environments of metro stations, UWB-based seamless payment suffers from limited positioning accuracy and insufficient stability. A promising solution is to incorporate the vision modality, thereby enhancing localization robustness through cross-modal trajectory alignment. Nevertheless, high similarity among passenger trajectories, modality imbalance between vision and UWB, and UWB drift in crowded conditions collectively pose substantial challenges to trajectory alignment in metro stations. To address these issues, this paper proposes a multi-modal trajectory progressive alignment algorithm under modality imbalance. Specifically, a progressive alignment mechanism is introduced, which leverages the alignment probabilities from previous time steps to exploit the temporal continuity of trajectories, thereby gradually increasing confidence in alignments while mitigating the uncertainty of individual matches. In addition, contrastive learning with the InfoNCE loss is employed to enhance the model’s ability to learn from scarce but critical positive samples and to ensure stable matching on the UWB modality. Experimental results demonstrate that the proposed method consistently outperforms baseline approaches in both off-peak and peak periods, with its matching error rate reduced by 68% compared to the baseline methods during peak periods.