Visualization Analysis of Learning Attention Based on Single-image PnP Head Pose Estimation

Abstract

Learning attention analysis of students is the important indicator of classroom teaching/learning quantitative evaluation. Owing to the fact that the head-mounted eye tracker is expensive and unsuitable to be widely used in the large-scale classroom evaluation under expenditure limitation, in this paper, we uses the PnP(Perspective-nPoint) method to estimate student's head pose for single-image. And then we achieve visualization of learning attention. Experiments demonstrate the following advantages of our method. (1) The method limits the average head-pose estimation errors under 4.88° with Biwi database. (2) This work has implemented student learning attention visualization analyses for three typical learning cases including engagement, attention, and disregard.

Deep Learning for Head Pose Estimation: A Survey

“…Therefore, HPE can be used in smart rooms to monitor participants in a meeting and to record their activities, in particular, their attention can be indirectly related to their head pose [11]. Systems exploiting head pose estimation to analyse people behaviour and human interaction in meeting and workplaces have been proposed in [12][13][14].…”

Online Classroom Evaluation System Based on Multi-Reaction Estimation

“…Humans use their head orientation to convey information during interpersonal interactions, for example, a listener nods to a speaker to indicate that he/she understands the information being communicated, or a listener pulls his/her head back to indicate avoidance or disapproval. Li [6] proposed a method that estimates the attention of 30 students in a class using a camera that real-time detects their head rotation without recognizing the eyeball pose; subsequently, they visualized the three states of student learning. Xu [11] proposed a multiple Euler angle constraint method to create a scoring module to analyze students' attention based on head pose estimation, where the system reported evaluates student attention levels from 0.0 to 1.0.…”