ANALOGICAL REPRESENTATION OF SPACE AND TIME

In computer vision, the usual level of 'interpretation' is the identification of the objects in the image. In this paper, we extend the level of interpretation to include spatial event detection using a knowledge base for a known scene. This will allow us to formulate a computational theory for forming conceptual descriptions about the behaviours of the objects. Here we describe an analogical representation of space and time that supports the formation of an event level description giving a local interpretation. These, in turn, can be used to form the global level conceptual descriptions. The analogical representation provides a characterization of the real world making explicit the behavioural data that is usually implicit. The knowledge base includes information about the scene layout and expected behaviours of the scene objects. Some of this behavioural information is spatially invariant and is used to contextually index the scene. The anlogical reasoning uses this contextual indexing of the spatial knowledge to provide a behavioural interpretation that describes what the objects are doing in the scene. We illustrate the approach with examples of road traffic surveillance at a German roundabout.