Abstract
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 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 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.
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Warden, T., Lattner, A.D., Visser, U. (2009). Real-Time Spatio-Temporal Analysis of Dynamic Scenes in 3D Soccer Simulation. In: Iocchi, L., Matsubara, H., Weitzenfeld, A., Zhou, C. (eds) RoboCup 2008: Robot Soccer World Cup XII. RoboCup 2008. Lecture Notes in Computer Science(), vol 5399. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02921-9_32
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DOI: https://doi.org/10.1007/978-3-642-02921-9_32
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