SeeCoast: persistent surveillance and automated scene understanding for ports and coastal areas

Bradley J. Rhodes; Neil A. Bomberger; Todd M. Freyman; William Kreamer; Linda Kirschner; Adam C. L’Italien; Wendy Mungovan; Chris Stauffer; Lauren Stolzar; Allen M. Waxman; Michael Seibert

doi:10.1117/12.725627

1 May 2007 SeeCoast: persistent surveillance and automated scene understanding for ports and coastal areas

Bradley J. Rhodes, Neil A. Bomberger, Todd M. Freyman, William Kreamer, Linda Kirschner, Adam C. L’Italien, Wendy Mungovan, Chris Stauffer, Lauren Stolzar, Allen M. Waxman, Michael Seibert

Author Affiliations +

Proceedings Volume 6578, Defense Transformation and Net-Centric Systems 2007; 65781M (2007) https://doi.org/10.1117/12.725627
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

Abstract

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.

Citation Download Citation

Bradley J. Rhodes, Neil A. Bomberger, Todd M. Freyman, William Kreamer, Linda Kirschner, Adam C. L’Italien, Wendy Mungovan, Chris Stauffer, Lauren Stolzar, Allen M. Waxman, and Michael Seibert "SeeCoast: persistent surveillance and automated scene understanding for ports and coastal areas", Proc. SPIE 6578, Defense Transformation and Net-Centric Systems 2007, 65781M (1 May 2007); https://doi.org/10.1117/12.725627

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