An RGB-D based social behavior interpretation system for a humanoid social robot

Aolfazl Zaraki, Manuel Giuliani, Maryam Banitalebi Dehkordi, Daniele Mazzei, Annamaria D'Ursi, Danilo De Rossi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

Humanoid social robots that interact with people need to be capable of interpreting the social behavior of their interaction partners in order to respond in a socially appropriate way. In this paper, we present a social behavior interpretation system that enables a humanoid robot to recognize human social behavior by analyzing communicative signals. The system receives the constructed RGB-D scene from a Kinect sensor, extracts information about body gesture and head pose from the scene using Microsoft Kinect SDK, and recognizes eight human social behaviors using a Hidden Markov Model (HMM). We trained the eight-state HMM with a corpus of 35 recorded human-human interaction scenes. The evaluation of the system shows a weighted average recognition rate of 81% for all states.

Original languageEnglish
Title of host publication2014 2nd RSI/ISM International Conference on Robotics and Mechatronics, ICRoM 2014
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages185-190
Number of pages6
ISBN (Electronic)9781479967438
DOIs
Publication statusPublished - 17 Dec 2014
Event2014 2nd RSI/ISM International Conference on Robotics and Mechatronics, ICRoM 2014 - Tehran, Iran, Islamic Republic of
Duration: 15 Oct 201417 Oct 2014

Publication series

Name2014 2nd RSI/ISM International Conference on Robotics and Mechatronics, ICRoM 2014

Conference

Conference2014 2nd RSI/ISM International Conference on Robotics and Mechatronics, ICRoM 2014
Country/TerritoryIran, Islamic Republic of
CityTehran
Period15/10/1417/10/14

Keywords

  • hidden Markov model
  • Human-robot interaction
  • humanlike robot
  • social behavior recognition

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