Pose and attention mechanism based behavior recognition method and its application in education

Behavior recognition is an important research domain in computer vision. Extracting human motion features and identifying behaviors from video can provide important support for education, medical treatment, security, and other fields. However, current mainstream behavior recognition methods prove ineffective in the challenging context of recognizing students’ classroom behavior due to scene complexity, numerous interfering factors, and lack of public datasets. To address these limitations, this paper presents a novel recognition model that integrates behavioral pose information and attention mechanisms. The proposed model employs a residual convolutional neural network with an attention mechanism to extract spatial and temporal features from teaching videos while leveraging behavioral pose information to enhance the description of visual cues and capture high-level behavioral information embedded in depth features. To assess the model's effectiveness, we create a comprehensive dataset of students' classroom behavior and conduct extensive experiments. The results demonstrate the proposed model's high performance in recognizing college students' classroom behavior.