This paperpresent a novel framework for inferring global behaviour patterns through modelling behaviour correlations in a wide-area scene and detecting any

anomaly in behaviours occurring both locally and globally. Specifically,

This paperpropose a semantic scene segmentation model to decompose a wide-area

scene into regions where behaviours share similar characteristic and are represented as classes of video events bearing similar features. To model behavioural correlations globally, This paperinvestigate both a probabilistic Latent Semantic Analysis (pLSA) model and a two-stage hierarchical pLSA model for

global behaviour inference and anomaly detection. The proposed framework

is validated by experiments using complex crowded outdoor scenes.