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We present a novel approach for the visual prediction of human-objectinteractions in videos. Rather than forecasting the human and object motion orthe future hand-object contact points, we aim at predicting (a)the class of theon-going human-object interaction and (b) the class(es) of the next activeobject(s) (NAOs), i.e., the object(s) that will be involved in the interactionin the near future as well as the time the interaction will occur. Graphmatching relies on the efficient Graph Edit distance (GED) method. Theexperimental evaluation of the proposed approach was conducted using twowell-established video datasets that contain human-object interactions, namelythe MSR Daily Activities and the CAD120. High prediction accuracy was obtainedfor both action prediction and NAO forecasting.

Graphing the Future: Activity and Next Active Object Prediction using Graph-based Activity Representations