Due to the large number of students in a typical classroom and crowded seating, most features of student posture are often obscured, making it difficult to balance the accuracy in identifying student postures with computational efficiency. To solve this issue, a novel classroom student posture recognition method is proposed. First, to recognize the poses of multiple students in the classroom, we use the you-only-look-once (YOLOv3) algorithm for object detection and retrain it to detect human objects that are hunching on a table, creating the pose estimation network. Next, to improve the accuracy of the pose estimation network, we use the squeeze-and-excitation network structure that is embedded in the residual structure of high-resolution networks (HRNet). Finally, with the improved HRNet algorithm’s outputs of key human body points, we design a pose classification algorithm based on a support vector machine, to classify human poses in the classroom. Experiments show that the improved HRNet multi-person pose estimation algorithm yields the best mean average precision performance of 73.76% on the common objects in context (COCO) validation dataset. We further test the proposed algorithm on a customer dataset collected in a classroom and achieved a high recognition rate of 90.1% and good robustness.

由于典型教室中学生人数众多且座位拥挤，学生姿势的大部分特征往往被掩盖，难以在识别学生姿势的准确性与计算效率之间取得平衡。针对这一问题，提出了一种新颖的课堂学生姿势识别方法。首先，为了识别课堂上多个学生的姿势，我们使用您只看一次 (YOLOv3) 算法进行对象检测，并对其进行重新训练以检测在桌子上弯腰的人体对象，从而创建姿势估计网络。接下来，为了提高姿态估计网络的准确性，我们使用嵌入在高分辨率网络（HRNet）残差结构中的挤压激励网络结构。最后，结合改进后的 HRNet 算法对人体关键点的输出，我们设计了一种基于支持向量机的姿势分类算法，对课堂中的人体姿势进行分类。实验表明，改进的 HRNet 多人姿态估计算法在上下文（COCO）验证数据集中的常见对象上产生了 73.76% 的最佳平均精度性能。我们在课堂上收集的客户数据集上进一步测试了所提出的算法，并获得了 90.1% 的高识别率和良好的鲁棒性。