Combining Perception Considerations with Artificial Intelligence in Maritime Threat Detection Systems

Over the past few years the need for early-warning maritime threat detection systems has dramatically increased. Our research aims to address this need by tackling three main problems: 1) classify boat activities into three categories: random walk, following, and chasing, 2) real-time classification of boat path trajectories, and 3) designing a novel perception-based framework for activity detection in the maritime context. We propose the implementation of an entropy-based detection algorithm, trained using synthetic data. We assess the viability of the proposed framework based on accuracy and the number of time steps required prior to identification. The synthetic data generated has the potential to spur other research efforts in the field of maritime detection.