Visible to the public Semantic Information Fusion to Enhance Situational Awareness in Surveillance Scenarios

TitleSemantic Information Fusion to Enhance Situational Awareness in Surveillance Scenarios
Publication TypeConference Paper
Year of Publication2017
AuthorsMüUller, W., Kuwertz, A., Mühlenberg, D., Sander, J.
Conference Name2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)
ISBN Number978-1-5090-6064-1
Keywordsaerospace computing, autonomous aerial vehicles, civil protection, Cognition, critical events detection, Data integration, data mining, Databases, enhanced situational awareness, high-level data fusion component, inference mechanisms, information integration, intelligence-surveillance-and-reconnaissance analytics architecture, ISR-AA, knowledge model, logical reasoning, Markov logic network, Markov processes, military applications, military computing, National security, Network reconnaissance, Object oriented modeling, object-oriented methods, object-oriented world model, ontologies (artificial intelligence), Ontology, OOWM, probabilistic information processing, Probabilistic logic, probabilistic reasoning, pubcrawl, reasoning component, Resiliency, security forces, semantic information fusion, sensor data, sensor fusion, situational awareness, situational picture, surveillance, surveillance scenarios, UAS, unmanned aircraft systems, video surveillance

In recent years, the usage of unmanned aircraft systems (UAS) for security-related purposes has increased, ranging from military applications to different areas of civil protection. The deployment of UAS can support security forces in achieving an enhanced situational awareness. However, in order to provide useful input to a situational picture, sensor data provided by UAS has to be integrated with information about the area and objects of interest from other sources. The aim of this study is to design a high-level data fusion component combining probabilistic information processing with logical and probabilistic reasoning, to support human operators in their situational awareness and improving their capabilities for making efficient and effective decisions. To this end, a fusion component based on the ISR (Intelligence, Surveillance and Reconnaissance) Analytics Architecture (ISR-AA) [1] is presented, incorporating an object-oriented world model (OOWM) for information integration, an expressive knowledge model and a reasoning component for detection of critical events. Approaches for translating the information contained in the OOWM into either an ontology for logical reasoning or a Markov logic network for probabilistic reasoning are presented.

Citation Keymuuller_semantic_2017