This paper presents an extensible architectural model for general content-based analysis and indexing of video data which can be customised for a given problem domain. Video interpretation is approached as a joint inference problems which can be solved through the use of modern machine learning and probabilistic inference techniques. An important aspect of the work concerns the use of a novel active knowledge representation methodology based on an ontological query language. This representation allows one to pose the problem of video analysis in terms of queries expressed in a visual language incorporating prior hierarchical knowledge of the syntactic and semantic structure of entities, relationships, and events of interest occurring in a video sequence. Perceptual inference then takes place within an ontological domain defined by the structure of the problem and the current goal set.
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