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This paper presents a novel method to predict future human activities frompartially observed RGB-D videos. Human activity prediction is generallydifficult due to its non-Markovian property and the rich context between humanand environments.  We use a stochastic grammar model to capture the compositional structure ofevents, integrating human actions, objects, and their affordances. We representthe event by a spatial-temporal And-Or graph (ST-AOG). The ST-AOG is composedof a temporal stochastic grammar defined on sub-activities, and spatial graphsrepresenting sub-activities that consist of human actions, objects, and theiraffordances. Future sub-activities are predicted using the temporal grammar andEarley parsing algorithm. The corresponding action, object, and affordancelabels are then inferred accordingly. Extensive experiments are conducted toshow the effectiveness of our model on both semantic event parsing and futureactivity prediction.

Predicting Human Activities Using Stochastic Grammar