SeeCoast is a prototype US Coast Guard port and coastal area surveillance system that aims to reduce operator workload while maintaining optimal domain awareness by shifting their focus from having to detect events to being able to analyze and act upon the knowledge derived from automatically detected anomalous activities. The automated scene understanding capability provided by the baseline SeeCoast system (as currently installed at the Joint Harbor Operations Center at Hampton Roads, VA) results from the integration of several components. Machine vision technology processes the real-time video streams provided by USCG cameras to generate vessel track and classification (based on vessel length) information. A multi-INT fusion component generates a single, coherent track picture by combining information available from the video processor with that from surface surveillance radars and AIS reports. Based on this track picture, vessel activity is analyzed by SeeCoast to detect user-defined unsafe, illegal, and threatening vessel activities using a rule-based pattern recognizer and to detect 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. The fused track picture also provides a basis for automated control and tasking of cameras to detect vessels in motion. Real-time visualization combining the products of all SeeCoast components in a common operating picture is provided by a thin web-based client.
SeeCoast extends the US Coast Guard Port Security and Monitoring system by adding capabilities to detect, classify, and
track vessels using electro-optic and infrared cameras, and also uses learned normalcy models of vessel activities in
order to generate alert cues for the watch-standers when anomalous behaviors occur. SeeCoast fuses the video data with
radar detections and Automatic Identification System (AIS) transponder data in order to generate composite fused tracks
for vessels approaching the port, as well as for vessels already in the port. Then, SeeCoast applies rule-based and
learning-based pattern recognition algorithms to alert the watch-standers to unsafe, illegal, threatening, and other
anomalous vessel activities. The prototype SeeCoast system has been deployed to Coast Guard sites in Virginia. This
paper provides an overview of the system and outlines the lessons learned to date in applying data fusion and automated
pattern recognition technology to the port security domain.