A D-S Theory and Uninorm Based Framework of Reasoning and Multi-Attribute Decision Making for Surveillance System

CCTV (Closed-Circuit Television) and sensor based intelligent surveillance systems have attracted considerable attentions in the field of public security affairs. To provide real time reaction in the case of a huge volume of the surveillance data, researchers have proposed event reasoning frameworks for modelling and inferring events of interest. However, they do not support decision making, which is very important for surveillance operators. To this end, this paper incorporate a function of decision making in an event reasoning framework, so that our model not only can perform event reasoning but also can predict, rank, and alarm threats according to uncertain information from multiple heterogeneous sources. In particular, we propose a multi-attribute decision making model, in which an object being watched is modelled as a multi-attribute event, where each attribute corresponds to a specific source, and the information from each source can be used to elicit a local threat degree of different malicious situations with respect to the corresponding attribute. Moreover, to assess an overall threat degree of an object being observed we also propose a method to fuse the conflict threat degrees regarding all the relevant attributes. Finally, we demonstrate the effectiveness of our framework by an airport security surveillance scenario.