Intelligent Student Behavior Analysis System for Real Classrooms

In this paper, we design an intelligent student behavior analysis system for recorded classrooms, which automatically detects hand-raising, standing, and sleeping behaviors of students. Detecting these behaviors is quite challenging mainly due to various scale behaviors, low resolution, and imbalanced behavior samples. To overcome the above-mentioned challenges, we first build a large-scale student behavior corpus from thirty schools, labeling these behaviors using bounding boxes frame-by-frame, which changes the behavior recognition problem into object detections. Then, we propose an improved Faster R-CNN, a classical object detection model, for student behavior analysis. Specifically, we first present a novel scale-aware detection head to overcome scale variations. Secondly, we propose a new feature fusion strategy to detect low-resolution behaviors while introduces little computation overhead. Thirdly, we utilize OHEM (Online Hard Example Mining) to alleviate severe class imbalances. Experiment results on our real corpus are increased by 3.4% mAP while maintaining a fast speed.