基于GainPose的学生课堂行为识别方法OA

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.