Rule-Based Video Interpretation Framework: Application to Automated Surveillance

Abstract

Video applications usually involve a large number of moving objects. Moving objects refer to semantic realworld entities denoting a coherent spatial region and being automatically computed by the continuity of spatial and temporal low-level features, such as color and motion. In surveillance application, spatial and temporal relationships among these objects should be efficiently supported and retrieved for abnormal event recognition. In this paper we emphasize on analyzing and interpreting video object motions for advanced surveillance application. We propose a rule-based system allowing moving objects description at the low level (spatio-temporal features and relationships) as well as at the semantic level (actions, events and interaction). The result of the proposed system is illustrated to detect context dependent events in surveillance.