Enhanced Identification Algorithm Based on Machine Learning for Students Abnormal Behavior in Intelligent Classrooms
To reduce the influence of lighting and camera perspective,and improve the recognition effect of abnormal student behavior in intelligent classrooms,a machine learning based algorithm for enhan-cing the recognition of abnormal student behavior in intelligent classrooms was proposed.Firstly,a hu-man posture expression form was constructed,and the human skeleton node information of students'behavior was extracted.Based on the temporal correlation characteristics of the human skeleton node information and combined with linear transformation,typical behavior features were enhanced.Then the acceleration of the human body posture center of mass was defined and the criteria for judging ab-normal student behavior was set to achieve student behavior recognition combining with CNN-10 net-work.The experimental results indicated that the enhanced typical student behavior traits could reflect differences in behavioral characteristics,and the false detection rate is relatively low.
万方数据
维普
