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A Deep Learning Recognition Method for Students' Abnormal Behaviors in Smart Classroom Teaching Scenarios.

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Název: A Deep Learning Recognition Method for Students' Abnormal Behaviors in Smart Classroom Teaching Scenarios.
Autoři: Yajun, Wu1 (AUTHOR) 1992014@ntnc.edu.cn
Zdroj: International Journal of High Speed Electronics & Systems. Dec2025, Vol. 34 Issue 4, p1-21. 21p.
Témata: FEATURE extraction, PSYCHOLOGY of students, ACQUISITION of data, DATA quality, CLASSROOMS
Abstrakt: In the wisdom classroom, the acquisition and processing of student behavior data is a complicated process. However, in the process of data acquisition, the equipment may be affected by noise interference factors such as illumination and occlusion, which leads to the deterioration of data quality. Therefore, a deep learning recognition method for students' abnormal behaviors in the intelligent classroom teaching scene is proposed. By modifying the selection range of random points, five branches are constructed to extract local features, obscured features and overall features, and HOG features are used to describe the shape edges of local targets to effectively detect shape features. The improved YOLOv3 uses Darknet-47 as the feature extraction network to calculate the outliers. Based on the principle of maximum dependence and threshold value, the identification model of students' abnormal behavior is designed. The experimental results show that the average false report rate of the design method is 5.65%, the crossover ratio is higher than 90%, the frame rate is maintained between 297.7 and 293.2 in all codes, and the recognition delay time is lower than 200 ms. [ABSTRACT FROM AUTHOR]
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Abstrakt:In the wisdom classroom, the acquisition and processing of student behavior data is a complicated process. However, in the process of data acquisition, the equipment may be affected by noise interference factors such as illumination and occlusion, which leads to the deterioration of data quality. Therefore, a deep learning recognition method for students' abnormal behaviors in the intelligent classroom teaching scene is proposed. By modifying the selection range of random points, five branches are constructed to extract local features, obscured features and overall features, and HOG features are used to describe the shape edges of local targets to effectively detect shape features. The improved YOLOv3 uses Darknet-47 as the feature extraction network to calculate the outliers. Based on the principle of maximum dependence and threshold value, the identification model of students' abnormal behavior is designed. The experimental results show that the average false report rate of the design method is 5.65%, the crossover ratio is higher than 90%, the frame rate is maintained between 297.7 and 293.2 in all codes, and the recognition delay time is lower than 200 ms. [ABSTRACT FROM AUTHOR]
ISSN:01291564
DOI:10.1142/S0129156425402918