Research on lightweight pose estimation networks for classroom behavior recognition

Classroom behavior recognition requires models to have fast inference speed, fewer parameters, and good robustness. Although existing lightweight models achieve commendable real-time performance, they fall short in accuracy when dealing with the complex scenarios typical of classroom environments, failing to meet practical requirements. To address this issue, we proposed the focused linear attention pose estimation network (FLAPose), which redesigned sectionalization attention (SA) to enhance the model′s capability to capture local information. Subsequently, focused linear attention (FLA) was employed to improve the model′s ability to learn skeletal information. Finally, bone loss was introduced as an auxiliary supervision mechanism to strengthen the model′s learning capacity in occluded and overlapping regions. The experimental results show that FLAPose outperforms the baseline network RTMPose by achieving an average accuracy improvement of 1.7% on the COCO dataset and 4.8% on the CS-Dataset, a model with fewer parameters and computational resources. Moreover, by leveraging TensorRT for acceleration and deploying the model on an inference server, we measured the frames per second (FPS) of the model on both GPU and CPU. FLAPose achieves an FPS exceeding 764.460 on the GPU and over 215.63 on the CPU, surpassing all other networks.