A Deep Graph Matching-Based Method for Trajectory Association in Vessel Traffic Surveillance

Vessel traffic surveillance in inland waterways extensively relies on the Automatic Identification Syst em (AIS) and video cameras. While video data only captures the visual appearance of vessels, AIS data serves as a valuable source of vessel identity and motion information, such as position, speed, and heading. To gain a comprehensive understanding of the behavior and motion of known-identity vessels, it is necessary to fuse the AIS-based and video-based trajectories. An important step in this fusion is to obtain the correspondence between moving targets by trajectory association. Thus, we focus solely on trajectory association in this work and propose a trajectory association method based on deep graph matching. We formulate trajectory association as a graph matching problem and introduce an attention-based flexible context aggregation mechanism to exploit the semantic features of trajectories. Compared to traditional methods that rely on manually designed features, our approach captures complex patterns and correlations within trajectories through end-to-end training. The introduced dustbin mechanism can effectively handle outliers during matching. Experimental results on synthetic and real-world datasets demonstrate the exceptional performance of our method in terms of trajectory association accuracy and robustness.