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A Spatio-Temporal Attention-Based Method for Detecting Student Classroom Behaviors
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
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