An Efficient Knowledge Base Management Scheme for Context Aware Surveillance

We propose architecture for multi-agent systems to retrieve and classify features extracted from images and videos of smart cameras. To enable cooperative inference between agents on cameras, structured representation of agents’ knowledge and abilities is required in the form of ontologies. Recognized features if properly structured and annotated, can be a useful source of information for context aware surveillance. This work builds a hierarchical inference data deployment structure and import related and required data to annotate rich data arriving from multiple sensor streams, in this case smart cameras. The annotation provides an impetus to the improvement of knowledge over time. Proactive deployment provides the main concepts and properties to model a hierarchical area ontology structure which can span a university campus or a city. We also define management policies to compare their performance for the wide area surveillance specifically.