This work presents a deep neural-network-based hierarchical graphical model for individual and group activity recognition in surveillance scenes. As the first step, deep networks are used to recognize activities of individual people in a scene. Then, a neural network-based hierarchical graphical model refines the predicted labels for each activity by considering dependencies between different classes. Similar to the inference mechanism in a probabilistic graphical model, the refinement step mimics a message-passing encoded into a deep neural network architecture. We show that this approach can be effective in group activity recognition and the deep graphical model improving recognition rates over baseline methods.
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