Probabilistic Detection of Pointing Directions for Human-Robot Interaction

Abstract

Deictic gestures - pointing at things in human-human collaborative tasks - constitute a pervasive, non-verbal way of communication, used e.g. to direct attention towards objects of interest. In a human-robot interactive scenario, in order to delegate tasks from a human to a robot, one of the key requirements is to recognize and estimate the pose of the pointing gesture. Standard approaches rely on full-body or partial-body postures to detect the pointing direction. We present a probabilistic, appearance-based object detection framework to detect pointing gestures and robustly estimate the pointing direction. Our method estimates the pointing direction without assuming any human kinematic model. We propose a functional model for pointing which incorporates two types of pointing, finger pointing and tool pointing using an object in hand. We evaluate our method on a new dataset with 9 participants pointing at 10 objects.