Predicting Human Actions Taking into Account Object Affordances

Abstract

Anticipating human intentional actions is essential for many applications involving service robots and social robots. Nowadays assisting robots must do reasoning beyond the present with predicting future actions. It is difficult due to its non-Markovian property and the rich contextual information. This task requires the subtle details inherent in human movements that may imply a future action. This paper presents a probabilistic method for action prediction in human-object interactions. The key idea of our approach is the description of the so-called object affordance, the concept which allows us to deliver a trajectory visualizing a possible future action. Extensive experiments were conducted to show the effectiveness of our method in action prediction. For evaluation we applied a new RGB-D activity video dataset recorded by the Sez3D depth sensors. The dataset contains several human activities composed out of different actions.