Modeling threat behaviors in surveillance video metadata for detection using an Analogical Reasoner

Video surveillance systems are generating much more imagery than can be cost-effectively analyzed by human analysts. One approach to the automated analysis of this imagery is to split the problem into video-to-metadata and metadata-to-interpretation tasks. We describe a system for the metadata-to-interpretation task that automatically extracts propositional graphs from the video metadata and generates graphs for use in an analogical reasoning system to detect threat behaviors that occurred in the original video scene.