A framework for integrating MPEG-7 knowledge templates into video surveillance applications

In this paper we propose a method of representing knowledge structures for visual content categorization and annotation in video surveillance applications. In order to achieve this purpose, we designed and implemented an ontology database for storing templates of domain-dependent moving objects and scene regions descriptions. The novelty of the system consists in the employment of a double layer, visual and semantic knowledge representation at the beginning of the video processing chain. As in similar approaches, bottom-up low-level feature extraction mechanisms are used, followed by top-down object classification using hypothesis proposal extracted from the ontology database. Instead of using top-down inference after the low-level processing steps, the two processes are entangled, such that the feature extraction algorithms are driven by logical and scenario-bound object and scene regions relations. The resulting content description is converted into MPEG-7 compliant representation and archived for later retrieval purposes