A learner behavior recognition and detection method based on human posture estimation

With the rapid development of information technology and smart classroom, the accuracy of learner behavior recognition in classroom has become more and more important. In the process of behavior recognition by means of information technology, including deep learning technology, it is often difficult to accurately identify learners' actions due to limb occlusion, motion blur and camera position. Meanwhile, frequent classroom management with the help of teachers will also lead to class interruption and destroy the overall fluency of teaching. Based on this problem, this work uses an improved pose estimation method based on single-stage key points and pose detection algorithm to classify and judge the movements of learners in the classroom for problems such as limb occlusion and motion ambiguity, so as to achieve the purpose of assisting the management of classroom teaching process. Finally, the results of the behavior recognition detection method proposed in this paper are compared with the marked action image data, and the experimental data show that the proposed method can meet the needs of learners' behavior recognition detection in the classroom to a certain extent.