Student Classroom Behavior Detection based on YOLOv7-BRA and Multi-Model
  Fusion
Fan Yang Tao Wang Xiaofei Wang
Accurately detecting student behavior in classroom videos can aid in
analyzing their classroom performance and improving teaching effectiveness.
However, the current accuracy rate in behavior detection is low. To address
this challenge, we propose the Student Classroom Behavior Detection system
based on based on YOLOv7-BRA (YOLOv7 with Bi-level Routing Attention ). We
identified eight different behavior patterns, including standing, sitting,
speaking, listening, walking, raising hands, reading, and writing. We
constructed a dataset, which contained 11,248 labels and 4,001 images, with an
emphasis on the common behavior of raising hands in a classroom setting
(Student Classroom Behavior dataset, SCB-Dataset). To improve detection
accuracy, we added the biformer attention module to the YOLOv7 network.
Finally, we fused the results from YOLOv7 CrowdHuman, SlowFast, and DeepSort
models to obtain student classroom behavior data. We conducted experiments on
the SCB-Dataset, and YOLOv7-BRA achieved an mAP@0.5 of 87.1%, resulting in a
2.2% improvement over previous results. Our SCB-dataset can be downloaded from:
https://github.com/Whiffe/SCB-datase