Accurately detecting student behavior from classroom videos is beneficial for
analyzing their classroom status and improving teaching efficiency. However,
low accuracy in student classroom behavior detection is a prevalent issue. To
address this issue, we propose a Spatio-Temporal Attention-Based Method for
Detecting Student Classroom Behaviors (BDSTA). Firstly, the SlowFast network is
used to generate motion and environmental information feature maps from the
video. Then, the spatio-temporal attention module is applied to the feature
maps, including information aggregation, compression and stimulation processes.
Subsequently, attention maps in the time, channel and space dimensions are
obtained, and multi-label behavior classification is performed based on these
attention maps. To solve the long-tail data problem that exists in student
classroom behavior datasets, we use an improved focal loss function to assign
more weight to the tail class data during training. Experimental results are
conducted on a self-made student classroom behavior dataset named STSCB.
Compared with the SlowFast model, the average accuracy of student behavior
classification detection improves by 8.94\% using BDSTA.

提出了一种用于检测学生课堂行为的时空注意力方法 (BDSTA)，该方法利用 SlowFast 网络生成视频的运动和环境信息特征图，然后应用时空注意力模块对特征图进行信息聚合、压缩和激励，从而获得时间、通道和空间维度上的注意力映射，并基于这些注意力映射进行多标签行为分类。实验结果表明，使用 BDSTA 相较于 SlowFast 模型，学生行为分类检测的平均准确率提高了 8.94%。