Bayesian Network based Abnormality Detection with Genetic Algorithm optimization

Abnormality Detection (AD), being the core part of intelligent surveillance systems, is calling for growing research interest due to its importance in providing higher efficiency and labor saving. In this paper, we propose a novel Bayesian Network (BN) based AD method for smart surveillance in scenes containing large scale viewpoint changes without model-relearning. In the proposed AD scheme, Reasoning Layer is introduced into BN to strengthen logical inferences, and a localized Genetic Algorithm (GA) is developed to optimize BN parameters and structure. With the expert knowledge aided BN structure modeling and GA based optimization, the proposed method can provide more robust detection experience with retained accuracy. Experiments on unlearned surveillance test sequences are shown to exhibit the validity of this method.