Incorporating Contextual Knowledge to Dynamic Bayesian Networks for Event Recognition

This paper proposes a new Probabilistic Graphical Model (PGM) to incorporate the scene, event object interaction and the event temporal contexts into Dynamic Bayesian Networks (DBNs) for event recognition in surveillance videos. We first construct the event DBNs for modeling the events from their own appearance and kinematic observations, and then extend the DBN to incorporate the contexts for boosting event recognition performance. Unlike the existing context methods, our model incorporates various contexts into one unified model. Experiments on natural scene surveillance videos show that the contexts can effectively improve the event recognition performance even with great challenges like large intra-class variations and low image resolution.