Probabilistic detection of pointing directions for human-robot interaction


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Shukla D., ERKENT Ö., Piater J.

International Conference on Digital Image Computing: Techniques and Applications, Adelaide, Australia, 23 - 25 November 2015, pp.601-608 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/dicta.2015.7371296
  • City: Adelaide
  • Country: Australia
  • Page Numbers: pp.601-608

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.