Publicação
From cues to engagement: a comprehensive survey and holistic architecture for computer vision-based audience analysis in live events
| datacite.subject.sdg | 09:Indústria, Inovação e Infraestruturas | |
| datacite.subject.sdg | 04:Educação de Qualidade | |
| datacite.subject.sdg | 12:Produção e Consumo Sustentáveis | |
| dc.contributor.author | Lemos, Marco | |
| dc.contributor.author | Cardoso, Pedro | |
| dc.contributor.author | Rodrigues, Joao | |
| dc.date.accessioned | 2026-02-27T14:06:51Z | |
| dc.date.available | 2026-02-27T14:06:51Z | |
| dc.date.issued | 2026-01-08 | |
| dc.description.abstract | The accurate measurement of audience engagement in real-world live events remains a significant challenge, with the majority of existing research confined to controlled environments like classrooms. This paper presents a comprehensive survey of Computer Vision AI-driven methods for real-time audience engagement monitoring and proposes a novel, holistic architecture to address this gap, with this architecture being the main contribution of the paper. The paper identifies and defines five core constructs essential for a robust analysis: Attention, Emotion and Sentiment, Body Language, Scene Dynamics, and Behaviours. Through a selective review of state-of-the-art techniques for each construct, the necessity of a multimodal approach that surpasses the limitations of isolated indicators is highlighted. The work synthesises a fragmented field into a unified taxonomy and introduces a modular architecture that integrates these constructs with practical, businessoriented metrics such as Commitment, Conversion, and Retention. Finally, by integrating cognitive, affective, and behavioural signals, this work provides a roadmap for developing operational systems that can transform live event experience and management through data-driven, real-time analytics. | eng |
| dc.description.sponsorship | ALGARVE-FEDER-01180500 | |
| dc.identifier.doi | 10.3390/mti10010008 | |
| dc.identifier.issn | 2414-4088 | |
| dc.identifier.uri | http://hdl.handle.net/10400.1/28287 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | MDPI | |
| dc.relation.ispartof | Multimodal Technologies and Interaction | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Affective computing | |
| dc.subject | Crowd engagement | |
| dc.subject | HCI | |
| dc.subject | Real-time engagement | |
| dc.subject | Real-time analytics | |
| dc.subject | Computer vision | |
| dc.subject | Emotion recognition | |
| dc.subject | Crowd behaviour | |
| dc.subject | Event monitoring | |
| dc.title | From cues to engagement: a comprehensive survey and holistic architecture for computer vision-based audience analysis in live events | eng |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.issue | 1 | |
| oaire.citation.title | Multimodal Technologies and Interaction | |
| oaire.citation.volume | 10 | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | Lemos | |
| person.familyName | Cardoso | |
| person.familyName | Rodrigues | |
| person.givenName | Marco | |
| person.givenName | Pedro | |
| person.givenName | Joao | |
| person.identifier.ciencia-id | 5F10-1C37-FE45 | |
| person.identifier.ciencia-id | 8A19-98F7-9914 | |
| person.identifier.orcid | 0009-0004-8727-4254 | |
| person.identifier.orcid | 0000-0003-4803-7964 | |
| person.identifier.orcid | 0000-0002-3562-6025 | |
| person.identifier.rid | G-6405-2013 | |
| person.identifier.scopus-author-id | 35602693500 | |
| person.identifier.scopus-author-id | 55807461600 | |
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