Automatic learning of conceptual knowledge in image sequences for human behavior interpretation

Presentado al 3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA-2007) celebrado en Girona (Spain) del 6 al 8 de junio.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.This work has been supported by the EC grant IST- 027110 for the HERMES project and by the Spanish MEC under projects TIN2006-14606 and and DPI-2004-541. J. Gonzàlez acknowledges the support of a Juan de la Cierva postdoctoral fellowship from the Spanish MEC.Peer Reviewe

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.Peer Reviewe