2026-09-20T17:35:20Zhttps://ecommons.cornell.edu/server/oai/requestoai:ecommons.cornell.edu:1813/1034942026-05-15T19:39:59Zcom_1813_35col_1813_47
Liu, Shenghao
Ferrari, Silvia
Hariharan, Bharath
2021-03-15T13:43:20Z
2021-03-15T13:43:20Z
2020-12
ProQuest Submission ID: 11132
ProQuest Publication ID: 28261367
https://hdl.handle.net/1813/103494
https://doi.org/10.7298/jz9n-5e90
13312101
71 pages
Understanding the interaction between people from images is yet a challenging task in recent years. Several approaches attempted to address this challenge by different proposed models and compact descriptors encoding the consistency between people's spatial-temporal body features or their overall activities. Among all human body features, the head pose provides a distinct description of an individual's attention and is considered a key feature to interpret social interactions. In this thesis, I present a novel approach to infer interaction among a group of people in a group conversation scenario using the Markov Random Field model. A novel interaction feature is proposed to represent the transactional segment using the head pose. Furthermore, I extend the approach to infer the hierarchical structure of a group with the contextual information, which improves the leading method in the analysis of interactions among people and detect the emergent leader of the group. The qualitative result shows the effectiveness of interaction inference using a state-of-art dataset.
en
nonverbal features
social interaction
social signal processing
Social Interaction Inference and Group Emergent Leadership Detection Using Head Pose
dissertation or thesis
https://newcatalog.library.cornell.edu/catalog/13312101
application/pdf
Mechanical Engineering
Cornell University
Master of Science
M.S., Mechanical Engineering
https://hdl.handle.net/1813/59810
Publication
Cornell Theses and Dissertations
Liu, Shenghao
5893a6ea-7af3-41d7-abc6-04bcd26ab5df
open.access
open.access
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="0d48d157-5021-4bd0-9b0c-e445e3ca446e">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>en</Language>
<Title>Social Interaction Inference and Group Emergent Leadership Detection Using Head Pose</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2020-12</PublicationDate>
<DOI>https://doi.org/10.7298/jz9n-5e90</DOI>
<Authors>
<Author>
<DisplayName>Liu, Shenghao</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<OrgUnit />
</Publisher>
</Publishers>
<Keyword>nonverbal features</Keyword>
<Keyword>social interaction</Keyword>
<Keyword>social signal processing</Keyword>
<Abstract>Understanding the interaction between people from images is yet a challenging task in recent years. Several approaches attempted to address this challenge by different proposed models and compact descriptors encoding the consistency between people's spatial-temporal body features or their overall activities. Among all human body features, the head pose provides a distinct description of an individual's attention and is considered a key feature to interpret social interactions. In this thesis, I present a novel approach to infer interaction among a group of people in a group conversation scenario using the Markov Random Field model. A novel interaction feature is proposed to represent the transactional segment using the head pose. Furthermore, I extend the approach to infer the hierarchical structure of a group with the contextual information, which improves the leading method in the analysis of interactions among people and detect the emergent leader of the group. The qualitative result shows the effectiveness of interaction inference using a state-of-art dataset.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>