Classroom Attention Analysis Based on Multiple Euler Angles Constraint and Head Pose Estimation

摘要

Classroom attention analysis aims to capture rich semantic information to analyze how the students are reacting to the lecture. However, there are some challenges for constructing a uniform attention model of students in the classroom. Each student is an individual and it is hard to make a unified judgment. The orientation of the head reflects the direction of attention, but changes in posture and space can interfere with the direction of attention. Aiming to solve these, this paper proposes a scoring module for converting the head Euler angle and attention in the classroom. This module takes the head Euler angle in three directions as input, and introduces spatial information to correct the angle. The key idea of the proposed method lies in introducing the mutual constraint of multiple Euler angles with the head spatial information, aiming to make attention model less susceptible to the difference of head information. The mutual constraint of multiple Euler angles can provide more accurate head information, while the head spatial information can be utilized to correct the angle. Extensive experiments using classroom video data demonstrate that the proposed method can achieve more accurate results.