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Introducing Ontological Realism for Semi-Supervised Detection and Annotation of Operationally Significant Activity in Surveillance Videos

As part of DARPA's Mind's Eye program, a video-analysis software platform able to detect operationally significant activity in videos is being developed. The goal is to describe such activity semi-automatically in terms of verb phrases mapped to a realism-based ontology that can be used to infer and even predict further activities that are not directly visible. We describe how Region Connection Calculus and its derivative, Motion Class Calculus, can be used together to link the spatiotemporal changes that pixel-aggregates undergo in video-displays to the corresponding changes of the objects in reality that were recorded and to linguistic descriptions thereof. We discuss how Ontological Realism can be used as a safeguard to drawing such correspondences naively.