Severity Level Assessment from Semantically Fused Video Content Analysis for Physical Threat Detection in Ground Segments of Space Systems

Disaster risks related to natural hazards are evolving gradually, albeit accelerating over time, the human-made and cyber threats are changing rapidly exploiting the increasing progress in technologies and the complex, highly interlinked, modern environment of critical infrastructures. Therefore, as these threats have been intensifying, the actions to strengthen the resilience of critical infrastructures should be step up, by understanding their complex systems as well as the multi-risks nature. In this landscape, the aim of this work focuses to propose a framework enables to identify potential human-made threats, generated by using physical means, and captured by heterogeneous sources (CCTVs, UAVs etc.). Advanced machine learning techniques provide analysis of events and useful information, which are fused semantically and estimate the severity level of the potential attack, serving the needs for real-time monitoring and mitigating the risk.