Perception and Analysis of Teaching Process Based on Video Understanding

The classroom serves as the core battleground for education.Monitoring and evaluating teachers' instructional activities in the classroom is an effective means of improving the quality of teaching.However,existing manual evaluation methods suffer 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 human-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 provide 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.