Abstract
Visual events occurring in video streams (such as human postures or more complex activities) are detected from a robust and generic region-based representation of the visual content and inferred using a spatio-temporal language that integrates domain-specific knowledge. More specifically, salient regions of activity are first extracted from the dynamic of the salient points along the scene. They are mapped to a vocabulary of the domain, using a state-of-the-art classifier, to describe the visual content in terms of semantic facts. Occurrences of events, modelled as assertions of a language representing spatio-temporal relationships between facts, are inferred from the description of videos by applying a forward-reasoning engine. An application to visual events retrieval in videos of meetings is presented as a test case.
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