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.

Global Behaviour Inference using PLSA.pdf

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