杭州师范大学学报（自然科学版）2026，Vol.25Issue(3)：296-308,13.DOI:10.19926/j.cnki.issn.1674-232X.2025.02.121
基于GainPose的学生课堂行为识别方法
A student classroom behavior recognition method based on GainPose
摘要
Abstract
To address the difficulty of behavior recognition caused by dense distribution and severe occlusion of students in real classroom scenarios,this paper proposes a student classroom behavior recognition method based on GainPose.An improved YOLOv7 embedded with the convolutional block attention module(CBAM)and a small object detection module is used as the human detector to achieve accurate student localization.GainPose,based on missing keypoint data prediction,is employed as the pose estimator,integrating the generative adversarial imputation network(Gain)to predict missing keypoint data,thereby obtaining complete upper-body keypoint information of students.A lightweight MobileNetV3 is adopted as the backbone network of the single person pose estimator(SPPE)to reduce computational complexity.According to the classroom scenario,the spatial graph convolution strategy of the spatial temporal graph convolutional network is adjusted to a more adaptive spatial configuration partitioning strategy for student classroom behavior classification.Experimental results show that after acceleration with Tensor RT,the proposed method achieves an inference time of 1.97 ms per image on GPU,a detection speed of 95.67 f/s,and a mean average precision of 96.79％,demonstrating its capability for real-time detection of student classroom behaviors.关键词
课堂行为/行为识别/目标检测/姿态估计/时空图卷积Key words
classroom behavior/behavior recognition/object detection/pose estimation/spatial temporal graph convolu-tion分类
信息技术与安全科学引用本文复制引用
刘芳,黄美晨,赵玲,田枫,曹茂俊,孙嘉伟..基于GainPose的学生课堂行为识别方法[J].杭州师范大学学报（自然科学版）,2026,25(3):296-308,13.基金项目
黑龙江省高等教育教学改革研究项目(SJGYB2024440) （SJGYB2024440）
