A Joint Trajectory Completion Method Combined Spatiotemporal Motion for Long-Span Bridge Surveillance System With Dead Zone

Current surveillance systems used on long-span bridges have monitoring dead zones, resulting in incomplete vehicle trajectories, whereas abnormal vehicle trajectories occurring within these dead zones are highly prone to traffic accidents. Obtaining complete vehicle trajectories within dead zones is challenging, yet crucial for identifying abnormal trajectories. To tackle this challenge, this article presents a joint trajectory completion (JTC) framework, which enables the completion of vehicle trajectories in dead zones with similar vehicle appearances and diverse camera views. An appearance-spatiotemporal fusion (ASTF) module is utilized to integrate motion spatiotemporal and appearance information to enhance vehicle representations. A Transformer-based bi-directional trajectory completion (TBTC) module with two encoder-decoder structures is further constructed to complete trajectories. Finally, a mathematical assessment strategy is established according to traffic rules to identify abnormal trajectories. Sufficient experimental results on the Hangzhou Bay dataset demonstrate the effectiveness of the proposed JTC method, which achieves an improvement in mAP of 7.4% for vehicle re-identification (ReID) and reduces the average RMSE by at least 0.2123 m for completion of vehicle trajectories. The completion of trajectories within dead zones facilitates the subsequent effective identification of abnormal trajectories and rapid accident warnings.