Intelligent Student Behavior Analysis System for Real Classrooms
Rui Zheng, Fei Jiang, Ruimin Shen
Abstract
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.