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

Authors:
Pau Baiget;Carles Fernández;Xavier Roca;Jordi Gonzàlez
Affiliations:
Computer Vision Center & Dept. de Cièèències de la Computació, Edifici O, Campus UAB, 08193 Bellaterra, Spain;Computer Vision Center & Dept. de Cièèències de la Computació, Edifici O, Campus UAB, 08193 Bellaterra, Spain;Computer Vision Center & Dept. de Cièèències de la Computació, Edifici O, Campus UAB, 08193 Bellaterra, Spain;Institut de Robòtica i Informàtica Industrial (UPC --- CSIC), Llorens i Artigas 4-6, 08028, Barcelona, Spain
Venue:
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Year:
2007

Citing 7
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Abstract

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 Logicand a behavior modeling tool called Situation Graph Tree. These tools are used to generate conceptual descriptions which semantically describe observed behaviors.