考虑时空核心区域的智慧课堂小样本异常行为识别
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作者单位:

1.广东科技学院;2.华南理工大学 国际教育学院

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中图分类号:

TP183

基金项目:

国家自然科学基金(61672483);广东省教育科学规划课题(2022GXJK588)


Small sample abnormal behavior recognition in smart Classroom considering spatio-temporal core region
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School of International Education,South China University of Technology

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    摘要:

    为解决智慧课堂异常行为识别领域中异常行为样本比例偏低、异常行为的特征提取不精准以及行为的时间和空间特征未考虑等缺陷,本文提出一种面向智慧课堂的时空核心区域增强小样本异常行为识别方法。首先,设计出包含空间自适应核心区域选取模块、长短时特征图时空关联单元等组成的识别框架;然后,考虑时间和空间精细化匹配机制,有针对性设计地在框架中设计时空精细化小样本损失函数,并在训练中加入梯度预先中断、增强局部特征随机数据和优化损失函数3种训练技巧;最后,选取公开的行为数据集HMDB51,以及选取国家教育资源服务平台上公开课程录像视频两种数据集,验证了本文所提识别算法的有效性。实验表明:本文所提算法在智慧课堂异常行为识别问题上表现优越,具有一定的参考价值。

    Abstract:

    In the field of abnormal behavior recognition in smart classroom, the proportion of abnormal behavior samples is low, the feature extraction of abnormal behavior is not accurate, and the temporal and spatial characteristics of behavior are not considered. To solve these problems, this paper proposes an enhanced small sample abnormal behavior recognition method in the core area for smart classroom. Firstly, a recognition framework consisting of a spatially adaptive core region selection module and a spatio-temporal correlation unit of long and short-term feature maps was designed. Then, considering the temporal and spatial refinement matching mechanism, a spatio-temporal refinement few-shot loss function was designed in the framework, and three training techniques were added to the training: gradient pre-interruption, enhancing local feature random data, and optimizing the loss function. Finally, the effectiveness of the proposed recognition algorithm is verified by selecting the public behavior data set HMDB51 and the public course video data set from the National Education Resource Service platform. Experiments show that the algorithm proposed in this paper performs superior in the problem of abnormal behavior recognition in smart classroom and has certain reference value.

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  • 收稿日期:2024-10-11
  • 最后修改日期:2024-10-11
  • 录用日期:2024-10-28
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