Automatic analysis of human behaviour is a fundamental prerequisite for the creation of machines that can effectively interact with- and 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.
%0 Conference Paper
%1 mueller23_mm
%A Müller, Philipp
%A Balazia, Michal
%A Baur, Tobias
%A Dietz, Michael
%A Heimerl, Alexander
%A Schiller, Dominik
%A Guermal, Mohammed
%A Thomas, Dominike
%A Brémond, Francois
%A Alexandersson, Jan
%A André, Elisabeth
%A Bulling, Andreas
%B Proceedings of the 31st ACM International Conference on Multimedia
%C New York, NY, USA
%D 2023
%I Association for Computing Machinery
%K behaviour, challenge dataset, engagement, hcics nonverbal vis
%P 9640–9645
%R 10.1145/3581783.3613851
%T MultiMediate '23: Engagement Estimation and Bodily Behaviour Recognition in Social Interactions
%U https://doi.org/10.1145/3581783.3613851
%X Automatic analysis of human behaviour is a fundamental prerequisite for the creation of machines that can effectively interact with- and 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.
%@ 9798400701085
@inproceedings{mueller23_mm,
abstract = {Automatic analysis of human behaviour is a fundamental prerequisite for the creation of machines that can effectively interact with- and 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.},
added-at = {2024-07-11T10:05:52.000+0200},
address = {New York, NY, USA},
author = {M\"{u}ller, Philipp and Balazia, Michal and Baur, Tobias and Dietz, Michael and Heimerl, Alexander and Schiller, Dominik and Guermal, Mohammed and Thomas, Dominike and Br\'{e}mond, Fran\c{c}ois and Alexandersson, Jan and Andr\'{e}, Elisabeth and Bulling, Andreas},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/20abc5bed4ed934cce81f7310aefc1413/hcics},
booktitle = {Proceedings of the 31st ACM International Conference on Multimedia},
doi = {10.1145/3581783.3613851},
interhash = {ca3a2a6a03ea382f79c3c0c0d6355280},
intrahash = {0abc5bed4ed934cce81f7310aefc1413},
isbn = {9798400701085},
keywords = {behaviour, challenge dataset, engagement, hcics nonverbal vis},
location = {Ottawa ON, Canada},
numpages = {6},
pages = {9640–9645},
publisher = {Association for Computing Machinery},
series = {MM '23},
timestamp = {2024-07-11T10:11:36.000+0200},
title = {MultiMediate '23: Engagement Estimation and Bodily Behaviour Recognition in Social Interactions},
url = {https://doi.org/10.1145/3581783.3613851},
year = 2023
}
