We present a hierarchical model for human activity recognition in entire multi-person scenes. Our model describes human behavior at multiple levels of detail, ranging from low-level actions through to high-level events. We also include a model of social roles, the expected behaviors of certain people, or groups of people, in a scene. The hierarchical model includes these varied representations and various forms of interactions between people present in a scene. The model is trained in a discriminative max-margin framework. Experimental results demonstrate that this model can improve performance at all considered levels of detail, on two challenging datasets.
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