GESCAM : A Dataset and Method on Gaze Estimation for Classroom Attention Measurement

Abstract

Human gaze provides crucial insights into individual attention during social or educational interactions. Attention systems often rely on head and facial features to predict gaze direction, but reliable gaze target detection (GTD) requires rich contextual cues. These cues inform the system about an individual’s position within a scene and the surrounding objects they might be interacting with. Our paper proposes attention measurement using GTD in educational classrooms, leveraging a synthetic dataset called GESCAM (Gaze Estimation based Synthetic Classroom Attention Measurement). This dataset was meticulously generated using 3D modelling, animation, simulation, and rendering techniques comprising 60,000 images with 650,000 instances of individuals (students, teachers) engaged in various activities, including looking at blackboard, notebooks, mobile phones etc. Our novel network trained on GESCAM proficiently identifies gaze fixations within complex classroom scenes, offering insights into human attention in classrooms across diverse contexts.