Knowledge representation for the generation of quantified natural language descriptions of vehicle traffic in image sequences

Abstract

Our image sequence interpretation process, which generates conceptual descriptions of the behaviour of vehicles in real-world traffic scenes, essentially treated only a single vehicle (the agent) so far. Simultaneous behaviours of other vehicles in the scene have been formulated only relative to the agent. The approach discussed in this contribution allows us to quantify occurrences and thus to generate more global conceptual descriptions of behaviour by using natural language quantifiers. The semantics of such quantified occurrences are represented by special logic structures. Natural language descriptions are derived from these internal knowledge representations.