Trajectory Anomaly Detection

Trajectory anomaly detection focuses on identifying unusual patterns in movement data, such as GPS tracks or video-based motion capture, aiming to pinpoint deviations from expected behavior. Current research emphasizes developing robust and efficient algorithms, including neural collaborative filtering, language models treating trajectories as sequences, and physics-guided approaches for handling missing data, often incorporating techniques like contrastive learning and attention mechanisms. These advancements have significant implications for various fields, improving safety and security in transportation, surveillance, and maritime monitoring, as well as enhancing the reliability of autonomous systems.