Detail publikace

Gaze and Conversational Engagement in Multiparty Video Conversation: An annotation scheme and classification

Originální název

Gaze and Conversational Engagement in Multiparty Video Conversation: An annotation scheme and classification

Anglický název

Gaze and Conversational Engagement in Multiparty Video Conversation: An annotation scheme and classification

Jazyk

en

Originální abstrakt

When using a multiparty video mediated system, interacting partic-ipants assume a range of various roles and exhibit behaviors accord-ing to how engaged in the communication they are. In this paper we focus on estimation of conversational engagement fromgaze signal. In particular, we present an annotation scheme for conversational engagement, a statistical analysis of gaze data across varying levels of engagement, and we classify vectors of computed eye tracking measures. The results show that in 74% of cases the level of en-gagement can be correctly classified.

Anglický abstrakt

When using a multiparty video mediated system, interacting partic-ipants assume a range of various roles and exhibit behaviors accord-ing to how engaged in the communication they are. In this paper we focus on estimation of conversational engagement fromgaze signal. In particular, we present an annotation scheme for conversational engagement, a statistical analysis of gaze data across varying levels of engagement, and we classify vectors of computed eye tracking measures. The results show that in 74% of cases the level of en-gagement can be correctly classified.

BibTex


@inproceedings{BUT97049,
  author="Roman {Bednařík} and Shahram {Eivazi} and Michal {Hradiš}",
  title="Gaze and Conversational Engagement in Multiparty Video Conversation: An annotation scheme and classification",
  annote="When using a multiparty video mediated system, interacting partic-ipants assume
a range of various roles and exhibit behaviors accord-ing to how engaged in the
communication they are. In this paper we
focus on estimation of conversational engagement fromgaze signal.
In particular, we present an annotation scheme for conversational
engagement, a statistical analysis of gaze data across varying levels
of engagement, and we classify vectors of computed eye tracking
measures. The results show that in 74% of cases the level of en-gagement can be
correctly classified.",
  address="Association for Computing Machinery",
  booktitle="4th Workshop on Eye Gaze in Intelligent Human Machine Interaction",
  chapter="97049",
  doi="10.1145/2401836.2401846",
  edition="NEUVEDEN",
  howpublished="online",
  institution="Association for Computing Machinery",
  year="2012",
  month="october",
  pages="1--5",
  publisher="Association for Computing Machinery",
  type="conference paper"
}