MultiMediate '23: Engagement Estimation and Bodily Behaviour Recognition in Social Interactions
- Others:
- Deutsches Forschungszentrum für Künstliche Intelligenz GmbH = German Research Center for Artificial Intelligence (DFKI)
- Spatio-Temporal Activity Recognition Systems (STARS) ; Inria Sophia Antipolis - Méditerranée (CRISAM) ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
- University of Augsburg (UNIA)
- University of Stuttgart
- ACM
Description
Automatic analysis of human behaviour is a fundamental prerequisite for the creation of machines that can effectively interact withand support humans in social interactions. In MultiMediate '23, we address two key human social behaviour analysis tasks for the first time in a controlled challenge: engagement estimation and bodily behaviour recognition in social interactions. This paper describes the MultiMediate '23 challenge and presents novel sets of annotations for both tasks. For engagement estimation we collected novel annotations on the NOvice eXpert Interaction (NOXI) database. For bodily behaviour recognition, we annotated test recordings of the MPIIGroupInteraction corpus with the BBSI annotation scheme. In addition, we present baseline results for both challenge tasks.
Abstract
International audience
Additional details
- URL
- https://hal.science/hal-04330332
- URN
- urn:oai:HAL:hal-04330332v1
- Origin repository
- UNICA