Physics-Based and Human-Derived Information Fusion Video Activity Analysis

With ubiquitous data acquired from sensors, there is an ever increasing ability to abstract content from the combination of physics-based and human-derived information fusion (PHIF). The advancement of PHIF tools include graphical information fusion methods, target tracking techniques, and natural language understanding. Current discussions revolve around dynamic data-driven applications systems (DDDAS) which seeks to leverage high-dimensional modeling with real-time physical systems. An example of a model includes a learned dictionary that can be leveraged as information queries. In this paper, we discuss the DDDAS paradigm of sensor measurements, information processing, environmental modeling, and software implementation to deliver content for PHIF systems. Experimental results demonstrate the DDDAS-based Live Video Computing DataBase Modeling Systems (LVC-DBMS) approach affording data discovery and query-based flexibility for awareness to provide narratives of unknown situations.