Bayesian Analysis of Behaviors and Interactions for Situation Awareness in Transportation Systems

In large and crowded transportation sites such as ports, parking lots, or busy streets, the unsafe actions of a moving object (such as a pedestrian, a ship, or a car) may compromise the safety and security of the entire infrastructure. It follows that a full comprehension of the situations taking place in the area could significantly decrease the amount of work performed by human administrators and operators in charge of the zone, reducing also the impact of human errors. The idea behind this paper is to use trajectory data to analyze behaviors (atomic actions without any external influence) and interactions (actions inducted by the presence of another entity) of each target in the scene. Probabilistic techniques applied on a topological map of the scene allow tackling the problem in a robust way, handling the uncertainties arising in such environments. The system is tested in a real maritime scenario, where trajectory data of ships and vessels are used to determine normal and abnormal situations occurring in a canal.