Adaptive Maritime Video Surveillance

Maritime assets such as ports, harbors, and vessels are vulnerable to a variety of near-shore threats such as small-boat attacks. Currently, such vulnerabilities are addressed predominantly by watchstanders and manual video surveillance, which is manpower intensive. Automatic maritime video surveillance techniques are being introduced to reduce manpower costs, but they have limited functionality and performance. For example, they only detect simple events such as perimeter breaches and cannot predict emerging threats. They also generate too many false alerts and cannot explain their reasoning. To overcome these limitations, we are developing the Maritime Activity Analysis Workbench MAAW, which will be a mixed-initiative, real-time maritime video surveillance tool that uses an integrated supervised machine learning approach to label independent and coordinated maritime activities. It uses the same information to predict anomalous behavior and explain its reasoning. This is an important capability for watchstander training and for collecting performance feedback. In this paper, we describe MAAWs functional architecture, which includes the following pipeline of components 1 a video acquisition and preprocessing component that detects and tracks vessels in video images, 2 a vessel categorization and activity labeling component that uses standard and relational supervised machine learning methods to label maritime activities, and 3 an ontology-guided vessel and maritime activity annotator to enable subject matter experts e.g., watchstanders to provide feedback and supervision to the system. We report our findings from a preliminary system evaluation on river traffic video.