A Scene-Adaptive Trajectory Association Method for UAV Vision–AIS Applications in Airborne Maritime Perception

Unmanned aerial vehicle (UAV) video and Automatic Identification System (AIS) data provide high-frequency visual observations and low-frequency identity/location messages, respectively. However, these two types of sensor data differ in spatiotemporal dimensions and pose engineering challenges for association and matching. This paper proposes a scene-adaptive trajectory association framework for airborne maritime perception. First, visual trajectories are mapped into a geospatial coordinate system using camera, gimbal, and flight platform metadata. Second, sparse AIS position sequences are filtered with physical constraints and locally interpolated to reconstruct continuous trajectories. Finally, based on target motion significance and local candidate complexity, the framework dynamically switches between Instantaneous Synchronization Multi-Feature Association (ISMA) and Spatiotemporal Constraint Weighted Dynamic Time Warping (STC-WDTW), leveraging kinematic features to deeply optimize the matching strategy, significantly enhancing robustness in trajectory association. This study collects data from the Port of Tianjin and adjacent coastal waters and evaluates the proposed method based on this dataset. Experimental results demonstrate that the proposed method achieves 94.34% IDP, 92.02% IDR, 93.17% IDF1, and 86.50% MOFA. The proposed method can be applied to marine dynamic monitoring systems, providing technical support and practical value for vessel traffic management in complex maritime areas and multi-source data fusion.