A Single-Camera System for Teacher Visual Attention Proxy Analytics and Rule-Based Teaching Behavior Segmentation in Classroom Videos

Abstract

Scalable classroom process evidence can support teacher reflection and expert review, but manual observation is labor-intensive and direct eye tracking is often too intrusive for routine classroom use. This paper presents a single-camera classroom video analytics system that converts ordinary classroom recordings into reviewable process evidence for teacher reflection. The system combines three-class head detection, teacher association and tracking, six-dimensional head-pose estimation for frontal teachers, pose smoothing with a multiplicative extended Kalman filter, gaze-cone projection, student and region hit testing, four-class rule-based behavior segmentation, and evidence export. In the perception experiments, the three-class head detector achieved an mAP@0.50 of 0.96, while frontal-teacher head-pose estimation yielded a mean angular error of 7.55°. In behavior verification, the algorithm achieved 78.8% sample-wise accuracy and a weighted F1-score of 77.1% against adjudicated human labels. The complete pipeline processed a single 1080p video stream at 48.20 FPS under the reported setup. Because the system infers visual attention from head pose, its output should be interpreted as a visual-attention proxy rather than as a direct measure of eye gaze. Its behavior labels describe observable classroom process patterns and are intended to support human review, not to evaluate teaching quality or individual participants. The exported heatmaps, regional timelines, behavior timelines, annotated videos, and structured reports provide traceable evidence that can be checked against the source recording.