Computer Vision Based Hybrid Classroom Attention Monitoring

This research presents a novel computer vision-based attention monitoring system designed for both online and offline contexts. Leveraging advanced image processing and machine learning algorithms, the system analyzes human gaze patterns, eye movements, and facial expressions to accurately gauge att...

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Bibliographic Details
Published in2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS) pp. 1 - 6
Main Authors Rawat, Saniya, Rodrigues, Malivia, Sheregar, Prateeksha, Wagaskar, Kalpita Ajinkya, Tripathy, Amiya Kumar
Format Conference Proceeding
LanguageEnglish
Published IEEE 28.06.2024
Subjects
Attention
Computer Vision
Education
Face recognition
Gaze tracking
IEEE
Image processing
Real-time systems
smart teaching evaluation
Streaming media
Web conferencing
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Summary:This research presents a novel computer vision-based attention monitoring system designed for both online and offline contexts. Leveraging advanced image processing and machine learning algorithms, the system analyzes human gaze patterns, eye movements, and facial expressions to accurately gauge attention levels. In online scenarios, the system employs real-time webcam-based gaze tracking and facial recognition to provide immediate insights into user engagement during activities like video conferencing and virtual meetings. For offline analysis, recorded video footage is retrospectively examined, facilitating applications in education, workplace productivity, and user experience assessments. Privacy considerations are addressed through the implementation of privacy-preserving techniques. Experimental results demonstrate the system's efficacy in monitoring attention dynamics across diverse settings, contributing to a deeper understanding of human attention in various domains.
DOI:10.1109/ICITEICS61368.2024.10624965