EPLC-Pose: A Lightweight Student Posture Recognition Network Under Panoramic Classroom

With the development of intelligence, concepts such as "smart classroom" have emerged, and the intelligent recognition of classroom behavior has become a hot research topic. However, the current intelligent recognition of classroom behavior still faces challenges such as complex single-person poses, severe occlusions, sampling from non-real teaching classrooms, and the lack of lightweight recognition networks. We aim to use deep learning-based human pose estimation methods to accurately estimate and predict student behaviors in real classrooms and design a network that prioritizes lightweight architecture. Therefore, in this paper, we propose a lightweight network called EPLC-Pose (Efficient Panoramic Lightweight Classroom Pose Network), a novel architectural approach based on the YOLOv8-Pose framework. The network’s backbone and neck components have been enhanced by replacing the traditional C2F module with the innovative C-UIB module. Due to the advanced design of the C-UIB module, the number of parameters has been significantly reduced. Additionally, to address the issue of limb occlusion, the SEAM attention mechanism is introduced in the Neck part. To evaluate our method, we have created a comprehensive panoramic classroom behavior pose dataset (CPKD) consisting of 6000 images. The network has shown competitive results on both the CPKD and MS COCO datasets. Compared to the baseline model, our approach achieves a 46.37% reduction in parameters, a 48.57% decrease in GFLOPS, and an improvement in mAP50 by 1% and 0.4% respectively.