Enhanced Situational Awareness and Security Assessment in Surveillance Systems Using Cloud Model Theory

Abstract

Cloud Model Theory is a mathematical method to model the two components of uncertainty, Randomness and Fuzziness in linguistic concepts. This paper introduces a method to assess information, make and fuse decisions in multi-layered sensing systems using the cloud model to process uncertainty. In this research, Cloud model theory is used to model information extracted from digital images taken for the purpose of monitoring and surveillance in multi-layered sensing systems. The extracted information is assessed using the cloud models to make decisions about the level of security situations in the area under surveillance. The threat level of any security situation can be considered as concepts and can be modeled using cloud models to produce assessments; these assessments are then fused to make a decision about the level of the security threat. Cloud models provided a robust methodology to make decisions and evaluate security situations which led to enhanced situation awareness. Test results showed that cloud models performed much better in comparison with just using standard fuzzy logic models.