| Maritime Threat Detection using Plan Recognition |
Nov 2012 |
8 pages |
| Authors:
Bryan Auslander; Kalyan M Gupta; David W Aha; NAVAL RESEARCH LAB WASHINGTON DC
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 | Existing algorithms for maritime threat detection employ a variety of normalcy models that are probabilistic and/or rule-based. Unfortunately, they can be limited in their ability to model the subtlety and complexity of multiple vessel types and their spatio-temporal events, yet their representation is needed to accurately detect anomalies in maritime scenarios. To address these limitations, we apply plan recognition algorithms for maritime anomaly detection. In particular, we examine hierarchical task ... |
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| Maritime Threat Detection Using Probabilistic Graphical Models |
Jan 2012 |
7 pages |
| Authors:
Bryan Auslander; Kalyan M Gupta; David W Aha; NAVAL RESEARCH LAB WASHINGTON DC
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| Maritime threat detection is a challenging problem because maritime environments can involve a complex combination of concurrent vessel activities, and only a small fraction of these may be irregular, suspicious, or threatening. Previous work on this task has been limited to analyses of single vessels using simple rule-based models that alert watchstanders when a proximity threshold is breached. We claim that Probabilistic Graphical Models (PGMs) can be used to more ... |
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| A Comparative Evaluation of Anomaly Detection Algorithms for Maritime Video Surveillance |
Jan 2011 |
15 pages |
| Authors:
Bryan Auslander; Kalyan M Gupta; David W Aha; KNEXUS RESEARCH CORP SPRINGFIELD VA
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| A variety of anomaly detection algorithms have been applied to surveillance tasks for detecting threats with some success. However, it is not clear which anomaly detection algorithms should be used for domains such as ground-based maritime video surveillance. For example, recently introduced algorithms that use local density techniques have performed well for some tasks, but they have not been applied to ground-based maritime video surveillance. Also, the reasons for the ... |
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| Towards Research on Goal Reasoning with the TAO Sandbox |
11 Aug 2009 |
5 pages |
| Authors:
Bryan Auslander; Matthew Molineaux; David W Aha; Allen Munro; Quentin Pizzini; NAVY CENTER FOR APPLIED RESEARCH IN ARTIFICIAL INTELLIGENCE WASHINGTON DC
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| We describe our progress on instrumenting a Navy software simulator for use in the context of intelligent agent research. The Tactical Action Officer (TAO) Sandbox, developed at the University of Southern California, is used by officers to train for specific Navy missions. NRL and Knexus Research Corporation have integrated this simulator with intelligent agents using the Lightweight Integration and Evaluation Testbed (LIET), thus permitting the agent to play the role ... |
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