Pubblicazioni

Versione: 1.1 Approvato

LDO-ID 2015-05-04-03

Titolo Autonomous Abnormal Behaviour Detection in Intelligence Surveillance and Reconnaissance Applications

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.

Autori Meo Rosa, Esposito Roberto, Viola Sergio, Mellano Valter, Choor Chee Ming, Botta Marco, Ciaramaglia Franco

Tipologia Paper for Seminar/Symposium/Conference

Media RTSI 2015 - International Forum on Research and Technologies for Society and Industry (16-18 September 2015, Torino, Italy)

Sito web

Anno 2015