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IBPRIA
2007
Springer

Automatic Learning of Conceptual Knowledge in Image Sequences for Human Behavior Interpretation

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Automatic Learning of Conceptual Knowledge in Image Sequences for Human Behavior Interpretation
This work describes an approach for the interpretation and explanation of human behavior in image sequences, within the context of a Cognitive Vision System. The information source is the geometrical data obtained by applying tracking algorithms to an image sequence, which is used to generate conceptual data. The spatial characteristics of the scene are automatically extracted from the resuling tracking trajectories obtained during a training period. Interpretation is achieved by means of a rule-based inference engine called Fuzzy Metric Temporal Horn Logic and a behavior modeling tool called Situation Graph Tree. These tools are used to generate conceptual descriptions which semantically describe observed behaviors.
Pau Baiget, Carles Fernández Tena, F. Xavie
Added 19 Oct 2010
Updated 19 Oct 2010
Type Conference
Year 2007
Where IBPRIA
Authors Pau Baiget, Carles Fernández Tena, F. Xavier Roca, Jordi Gonzàlez
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