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From cues to engagement: a comprehensive survey and holistic architecture for computer vision-based audience analysis in live events
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Resumo(s)
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.
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Palavras-chave
Affective computing Crowd engagement HCI Real-time engagement Real-time analytics Computer vision Emotion recognition Crowd behaviour Event monitoring