Teacher Attention Recognition Algorithm Based on Full-Range Head Pose Estimation
Research on teacher attention is valuable for evaluating classroom teacher behavior.However,existing teacher-attention recognition algorithms struggle to address issues such as extreme head pose angles.This study proposes a teacher-attention state recognition algorithm based on the 6DRepNet360 model.This algorithm effectively enhances the accuracy of head pose estimation algorithms under extreme angles.In contrast to traditional methods that rely on conditional judgments to classify teacher attention states,the designed teacher-attention classification model,based on a Support Vector Machine(SVM),enables precise recognition of attention states even under complex head pose angles.To further address the errors introduced by the algorithm stability and accuracy,this study introduces a data cleaning algorithm based on a sliding window,effectively improving the authenticity and reliability of the overall recognition results.Through a series of algorithm evaluations on the constructed CCNUTeacherState dataset,experimental results demonstrate that the proposed teacher-attention recognition algorithm achieves an accuracy of 90.67%on the CCNUTeacherState dataset.
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