Teaching behaviors recognition by combining deep learning-based human body detection and pose estimation
Yulu Peng, Shenglian Lu, Zhiliang Qiu, Jijie Wang · 2024
Evaluating the quality of classroom teaching is crucial for modern education. Due to subjective assessment and qualitative analysis, traditional teaching evaluation methods often hard to provide comprehensive and objective data support. In this study, we propose an innovative model named PoseTeach, which is designed to automatically and quantitatively recognize teaching actions in the classroom from video stream. PoseTeach firstly detected the region of the teacher located by using Faster R-CNN and then estimated the pose with HRNet, finally recognized teacher's actions with PoseTeach. This model Leverages PoseC3D as the backbone network and integrates a Spatiotemporal Attention Mechanism (STAM) and a Dilated Convolution Module (DCM) to better capture the spatial and temporal information of teaching actions. A self-constructed dataset, TeachMove, which contains video clips of various classroom teaching behaviors, was used to train and test our model. On our self-constructed dataset, PoseTeach achieved an accuracy of 67.9% for Top-1 accuracy, with an average accuracy (acc/mean1) of 62.83%, outperforming the benchmark model 's 60% in both metrics. This experimental results also demonstrate that the proposed PoseTeach performs excellently across multiple evaluation metrics, surpassing traditional models such as ST-GCN, 2s-AGCN, and STGCN++.