Classroom behavior recognition based on federated network

Zhi-lin Zhu; Jun-qiang Du; Xiao Jiang

doi:10.1117/12.2641446

10 November 2022 Classroom behavior recognition based on federated network

Zhi-lin Zhu, Jun-qiang Du, Xiao Jiang

Proceedings Volume 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022); 123480G (2022) https://doi.org/10.1117/12.2641446
Event: 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 2022, Zhuhai, China

Abstract

Aiming at the problem of low recognition rate of single network due to insufficient feature extraction in classroom behavior recognition, a dual network (VGG16 and ResNet50) model architecture (Student Recognition Net, SRN) is proposed. First, collect data and preprocess it to construct a student behavior dataset. Secondly, use Yolov3 to obtain the coordinate information of the students to facilitate subsequent adjustments to the data set. Finally, the output of the dual network is weighted and retrained to increase the extraction of feature information. Experiments show that the average behavior recognition accuracy of this model on the SBR-9 data set reaches 92.3%, and the accuracy of some behavior recognition is up to 98.2%, which is better than the behavior recognition effect of a single network.

Citation Download Citation

Zhi-lin Zhu, Jun-qiang Du, and Xiao Jiang "Classroom behavior recognition based on federated network", Proc. SPIE 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 123480G (10 November 2022); https://doi.org/10.1117/12.2641446

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KEYWORDS

Data modeling

Network architectures

Image enhancement

Video

Image processing

Feature extraction

Target detection

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