Student Attentiveness Classification Using Geometric Moments Aided Posture Estimation
Abstract
Body Posture provides enough information regarding the current state of mind of a person. This idea is used to implement a system that provides feedback to lecturers on how engaging the class has been by identifying the attentive levels of students. This is carried out using the posture information extracted with the help of Mediapipe. A novel method of extracting features from the key points returned by Mediapipe using geometric moments is proposed. Geometric moments aided features classification performs better than the general distances and angles features classification. In order to extend the single person pose classification to multi person pose classification, object detection is implemented. Feedback is generated regarding the entire lecture and provided as the output of the system.
Degree
M.Sc.
Advisors
Liu, Purdue University.
Subject Area
Artificial intelligence|Computer science
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