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Fuzzy MLNs and QSTAGs for Activity Recognition and Modelling with RUSH

Event and activity modelling and recognition is at the centre of situational awareness (SA). Being able to detect and extract useful semantic information from diverse data sources in the form of events and activities, an SA system may assist human operators with cognitive analysis, situation monitoring and to inform decision making. Recognition of realworld activities with complex spatial and temporal interactions in the presence of uncertainty has been a major line of research. In this respect, Statistical Relational Learning methods such as Markov Logic Networks (MLNs) have been shown to provide a powerful and promising framework, and efficiently leveraging their representational power for event and activity recognition are active research and development activities. In this paper we propose a complementary method to existing approaches, FQSTAG-MLN, that combines Fuzzy MLNs with Fuzzy Qualitative Spatio-Temporal Activity Graphs (FQSTAGs) toward achieving efficient and explainable activity modelling and recognition. With FQSTAG-MLNs, the complexity of real-world activities is addressed by combining the representational richness of high-level activity specification with compact local spatio-temporal narratives for low-level events; while the uncertainty of activity and world models is calibrated using probabilistic methods and the vagueness of observations is calibrated using fuzzy set theory. We describe its current implementation within RUSH, which aims to be a flexible reasoning and learning platform for situational awareness. We present a use case illustration and early results.