Anomalous Trajectory

Anomalous trajectory research focuses on identifying and characterizing deviations from expected movement patterns in various domains, from traffic flow to camera movements in film. Current efforts leverage diverse machine learning approaches, including convolutional neural networks for image-based trajectory analysis, physics-informed neural networks for modeling complex dynamics like smoke, and reinforcement learning for online anomaly detection in road networks. These advancements improve anomaly detection accuracy and efficiency across applications, offering valuable insights into system behavior and enabling more robust and explainable predictions in fields like autonomous driving and video surveillance.