Real-time spatio-temporal analysis of dynamic scenes

摘要

We propose a set of tools 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 and 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 a challenging environment: 3D soccer simulation. It substantiates real-time applicability of our approach under tournament conditions, based on a 5-Hz (a) precise and (b) noisy/incomplete perception. The approach is not limited to robot soccer. Instead, it can also be applied in other fields such as experimental biology and logistic processes.