An Extended Event Reasoning Framework for Decision Support under Uncertainty

Abstract

To provide in-time reactions to a large volume of surveillance data, uncertainty-enabled event reasoning frameworks for closed-circuit television and sensor based intelligent surveillance system have been integrated to model and infer events of interest. However, most of the existing works do not consider decision making under uncertainty which is important for surveillance operators. In this paper, we extend an event reasoning framework for decision support, which enables our framework to predict, rank and alarm threats from multiple heterogeneous sources. © Springer International Publishing Switzerland 2014.