Improved YOLOv5 for skeleton-based classroom behavior recognition
Wentian Niu, Xin Sun, Kaixiang Yi · Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022) · 2023
Classroom behavior is an important criterion for evaluating instructional efficacy. In comparison to other behaviors, the challenge of classroom behavior detection is primarily influenced by ambient light variables and the presence of too many targets to recognize, resulting in missed detection. Recent research has demonstrated that information about the human skeleton can be used to identify classroom conduct. As a result, we present an enhanced yolov5-based skeletal recognition system for detecting classroom behavior in this paper. First, the YOLOv5 detection algorithm is improved to extract target prospects for the problem of missed detection; then, the human skeleton information is obtained using the Alphapose framework; finally, the skeletal data is sent into a two-stream adaptive graph convolution network to allow for the accurate recognition of various classroom behaviors. According to extensive tests, the detection algorithm based on bone recognition improves detection accuracy and lowers the false detection rate.