Anomaly Detection using B-spline Control Points as Feature Space in Annotated Trajectory Data from the Maritime Domain

The detection of anomalies and outliers is an important task for surveillance applications as it supports operators in their decision making process. One major challenge for the operators is to keep focus and not to be overwhelmed by the amount of information supplied by different sensor systems. Therefore, helping an operator to identify important details in the incoming data stream is one possibility to strengthen their situation awareness. In order to achieve this aim, the operator needs a detection system with high accuracy and low false alarm rates, because only then the system can be trusted. Thus, a fast and reliable detection system based on b-spline representation is introduced. Each trajectory is estimated by its cubic b-spline representation. The normal behavior is then learned by different machine learning algorithm like support vector machines and artificial neural networks, and evaluated by using an annotated real dataset from the maritime domain. The results are compared to other algorithms.