Context augmented Dynamic Bayesian Networks for event recognition

• Context model simultaneously incorporates three contexts into baseline DBN model.
• Event temporal context describes event semantic relationships over time.
• We utilized various contexts in surveillance videos for event recognition.
• Context model effectively improves performance for real scene 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 baseline event DBNs for modeling the events from their own appearance and kinematic observations, and then augment the DBN with contexts to improve its event recognition performance. Unlike the existing context methods, our model incorporates various contexts simultaneously into one unified model. Experiments on real scene surveillance datasets with complex backgrounds show that the contexts can effectively improve the event recognition performance even under great challenges like large intra-class variations and low image resolution.