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Event learning is one of the most important problems in AI. However,notwithstanding significant research efforts, it is still a very complex task,especially when the events involve the interaction of humans or agents withother objects, as it requires modeling human kinematics and object movements.This study proposes a methodology for learning complex human-object interaction(HOI) events, involving the recording, annotation and classification of eventinteractions. For annotation, we allow multiple interpretations of a motioncapture by slicing over its temporal span, for classification, we useLong-Short Term Memory (LSTM) sequential models with Conditional Randon Field(CRF) for constraints of outputs. Using a setup involving captures ofhuman-object interaction as three dimensional inputs, we argue that thisapproach could be used for event types involving complex spatio-temporaldynamics.

Fine-grained Event Learning of Human-Object Interaction with LSTM-CRF