  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 with
other 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 event
interactions. For annotation, we allow multiple interpretations of a motion
capture by slicing over its temporal span, for classification, we use
Long-Short Term Memory (LSTM) sequential models with Conditional Randon Field
(CRF) for constraints of outputs. Using a setup involving captures of
human-object interaction as three dimensional inputs, we argue that this
approach could be used for event types involving complex spatio-temporal
dynamics.
