Real-Time Spatio-Temporal Analysis of Dynamic Scenes in 3D Soccer Simulation

We propose a framework for spatio-temporal real-time, analysis of dynamic scenes. It is designed to improve the grounding situation of autonomous agents in (simulated) physical domains. We introduce a knowledge processing pipeline ranging from relevance-driven compilation of a qualitative scene description to a knowledge-based detection of complex event awl action sequences, conceived as a spatio-temporal pattern matching problem. A methodology for the formalization of motion patterns and their inner composition is introduced and applied to capture human expertise about domain-specific motion situations. We present extensive experimental results from the 3D soccer simulation that substantiate the online applicability of our approach under tournament conditions. based on 5) Hz a) precise and b) noisy/incomplete perception.