Recognizing primitive interactions by exploring actor-object states

A probabilistic framework is proposed that automatically learns models in constrained states of actor-object interactions from videos based on the observation that at the moment of physical contact, both the motion and the appearance of actors are constrained by the target object.

Abstract

In this paper, we present a solution to the novel problem of recognizing primitive actor-object interactions from videos. Here, we introduce the concept of actor-object states. Our method is based on the observation that at the moment of physical contact, both the motion and the appearance of actors are constrained by the target object. We propose a probabilistic framework that automatically learns models in such constrained states. We use joint probability distributions to represent both actor and object appearances as well as their intrinsic spatio-temporal configurations. Finally, we demonstrate the applicability of our approach on series of human-object interaction classification experiments.