A Joint Statistical and Symbolic Anomaly Detection System: Increasing performance in maritime surveillance

The need for improving the capability to detect illegal or hazardous activities and yet reducing the workload of operators involved in various surveillance tasks calls for research on more capable automatic tools. To maximize their performance, these tools should be able to combine automatic capturing of normal behavior from data with domain knowledge in the form of human descriptions. In a proposed Joint Statistical and Symbolic Anomaly Detection System, statistical and symbolic methods are tightly integrated in order to detect the majority of critical events in the situation while minimizing unwanted alerts. We exemplify the proposed system in the domain of maritime surveillance.

[1]  Michael Bosse,et al.  SeeCoast port surveillance , 2006, SPIE Defense + Commercial Sensing.

[2]  Andrew W. Moore,et al.  Rule-based anomaly pattern detection for detecting disease outbreaks , 2002, AAAI/IAAI.

[3]  H. Lan,et al.  SWRL : A semantic Web rule language combining OWL and ruleML , 2004 .

[4]  Anders Holst,et al.  Incremental stream clustering and anomaly detection , 2008 .

[5]  Göran Falkman,et al.  Sequential Conformal Anomaly Detection in trajectories based on Hausdorff distance , 2011, 14th International Conference on Information Fusion.

[6]  Anders Holst,et al.  Incremental Stream Clustering for Anomaly Detection and Classification , 2011, SCAI.

[7]  Rikard Laxhammar,et al.  Anomaly detection for sea surveillance , 2008, 2008 11th International Conference on Information Fusion.

[8]  Rikard Laxhammar,et al.  Anomaly Detection in Trajectory Data for Surveillance Applications , 2011 .

[9]  Alexander Gammerman,et al.  Hedging predictions in machine learning , 2006, ArXiv.

[10]  Jean Roy,et al.  Anomaly detection in the maritime domain , 2008, SPIE Defense + Commercial Sensing.

[11]  Kenneth Baclawski,et al.  A core ontology for situation awareness , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.

[12]  Alan N. Steinberg,et al.  Revisions to the JDL data fusion model , 1999, Defense, Security, and Sensing.

[13]  Sangkyum Kim,et al.  ROAM: Rule- and Motif-Based Anomaly Detection in Massive Moving Object Data Sets , 2007, SDM.

[14]  Kathryn B. Laskey,et al.  Bayesian semantics for the semantic web , 2005 .

[15]  F. Johansson,et al.  Detection of vessel anomalies - a Bayesian network approach , 2007, 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information.

[16]  Kathryn B. Laskey.  MEBN: A language for first-order Bayesian knowledge bases , 2008, Artif. Intell..

[17]  Mieczyslaw M. Kokar,et al.  Using SWRL and OWL to Capture Domain Knowledge for a Situation Awareness Application Applied to a Supply Logistics Scenario , 2005, RuleML.

[18]  Egils Sviestins,et al.  Rule-based situation assessment for sea surveillance , 2006, SPIE Defense + Commercial Sensing.