SeeCoast: Automated Port Scene Understanding Facilitated by Normalcy Learning

SeeCoast is a prototype US Coast Guard (USCG) port surveillance system that provides automated scene understanding support for watchstanders. A major SeeCoast objective is to reduce operator workload while maintaining optimal domain awareness by shifting operators' focus from having to detect events to being able to analyze and act upon the knowledge derived from automatically detected anomalous activities. Analyst-defined vessel activities are recognized from pre-scripted patterns and anomalous vessel activities are detected using machine learning techniques. The baseline SeeCoast system interfaces to the USCG Hawkeye prototype and uses (a) machine vision technology to produce target tracks from streaming video data; (b) multi-INT fusion technology to correlate radar, automatic identification system (AIS), and/or video track data into a single coherent track picture; (c) vessel activity analysis and learning technology to provide alerts for events of interest according to user-defined criteria; and (d) visualization of those alerts embedded within the common operating picture. The video processing component tasks and controls Hawkeye cameras to detect vessels in motion and generates vessel track and classification (based on vessel length) information. SeeCoast detects unsafe, illegal, and threatening vessel activities using a rule-based pattern recognizer and detects anomalous vessel activities on the basis of automatically learned behavior normalcy models. Operators can optionally guide the learning system in the form of examples and counter-examples of activities of interest, and refine the performance of the learning system by confirming alerts or indicating examples of false alarms. This paper focuses on the learning-based activity analysis capabilities of SeeCoast