Perception and Analysis of Teaching Process Based on Video Understanding
The classroom serves as the core battleground for education.Monitoring and evaluating teachers'instructional activi-ties in the classroom is an effective means of improving the quality of teaching.However,existing manual evaluation methods suf-fer from drawbacks such as low efficiency,potential disruption of classroom dynamics,and subjective errors,making it difficult to achieve satisfactory results.Given the rapid development of artificial intelligence(AI)technology,it is proposed to integrate hu-man-centered intelligent analysis techniques into teachers'instructional processes for real-time recognition and analysis of tea-chers.First,a facial detection algorithm is employed to locate the teacher's position and estimate their movements.Second,a gaze estimation algorithm is utilized to detect the teachers'focal points.Lastly,skeleton-based action recognition and facial expression recognition are employed to perceive and analyze teachers'actions and expressions.Quantitative statistics on the indicators pro-vide a more efficient and objective understanding of teachers'teaching characteristics,so as to help teachers improve their tea-ching quality.As experimented in the same configuration environment,the modules of the system perform well in the correspon-ding tasks and fulfill the requirements in teaching scenarios.From the evaluation results on real-world teaching videos,the system is designed to accurately perceive the teachers'instructional states,providing constructive feedback for enhancing teaching quality.