Student Classroom Behavior Detection Based on Video Spatio-Temporal Features
Classroom behavior can effectively reflect students' learning status,and using deep learning technology to detect classroom behav-ior is of great significance for improving teaching methods and enhancing teaching quality.At present,classroom behavior detection is mostly based on static images,which often ignore the dynamic characteristics of behavior and have poor performance in detecting continuous behavior.To this end,a YOLOv7 SlowFast classroom behavior detection method based on video spatiotemporal features is proposed,which locates stu-dent targets through YOLOv7 and uses SlowFast to detect classroom behavior.Firstly,in order to improve the detection accuracy of YOLOv7 in densely populated environments,an adaptive spatial feature fusion module is introduced to solve the problem of inconsistency between features of different scales.Then,the RepGhost lightweight module is used to improve the YOLOv7 network structure,and the model detection speed is improved by reparameterizing the structure.Finally,to address the issue of low spatiotemporal behavior detection accuracy in SlowFast,a standardized time attention module is designed to enhance the model's perception of temporal features.The experimental results show that the improved YOLOv7 has an average precision mean(mAP)of 86.96%on the Crowdhuman dataset.After improvement,SlowFast achieved an mAP of 87.28%on a self-made classroom behavior detection dataset,which can effectively detect classroom behavior.
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