This paper describes a module that extracts rules or frequent patterns through data mining from a large database fed by targets detected by a Mission System installed on an unmanned airborne platform and the associated ground station to discover anomalies in local traffic. It has been demonstrated that the module is able to detect all tracks or targets present in the ground truth and also the paths followed by each tracks. Traffic anomalies can be detected by observing differences in extracted rules in reference missions compared to the current mission. The module will significantly reduce the operator workload as it can operate autonomously.

Autonomous Abnormal Behaviour Detection in Intelligence Surveillance and Reconnaissance Applications

MEO, Rosa;ESPOSITO, Roberto;BOTTA, Marco;
2015

Abstract

This paper describes a module that extracts rules or frequent patterns through data mining from a large database fed by targets detected by a Mission System installed on an unmanned airborne platform and the associated ground station to discover anomalies in local traffic. It has been demonstrated that the module is able to detect all tracks or targets present in the ground truth and also the paths followed by each tracks. Traffic anomalies can be detected by observing differences in extracted rules in reference missions compared to the current mission. The module will significantly reduce the operator workload as it can operate autonomously.
International Forum on Research and Technologies for Society and Industry
Torino
16-18 September
First International Forum on Research and Technologies for Society and Industry
IEEE
1
7
http://rtsi2015.tr.unipg.it/technical_program.htm
Remote Sensing; UAV; surveillance; data mining; inductive database; descriptive languages; frequent patterns; anomaly;
Meo, Rosa; Esposito, Roberto; Botta, Marco; Sergio, Viola; Choor, C. M.; Valter, Mellano; Franco, Ciaramaglia
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/1525435
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