Students’ Classroom Behavior Detection Based on Human-Object Interaction Model

Yonghe Zhang, Wenjiao Qu, Guocheng Zhong, Yundan Xiao
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Abstract

Existing classroom behavior detection methods for students are mainly based on the network model to extract key common features to directly determine behavior types, which cannot provide a higher fine-grained understanding of interaction relationships in the classroom. This paper proposes a classroom behavior detection method for students based on the Human-Object Interaction (HOI) model, which further utilizes human-object relationship features to infer interaction relationships based on object detection. In the study, the cell phone is selected as the detected object to interact with the students, and the HOI model is trained and tested for two types of behaviors—Use and No interaction. The results show that the average accuracy of the trained HOI model reaches about 83.4% in the test, which promotes a higher fine-grained perception and understanding of classroom behavior detection and provides a new perspective for building smart classrooms and exploring personalized teaching and learning paths.
基于人-物交互模型的学生课堂行为检测
现有的学生课堂行为检测方法主要是基于网络模型提取关键的共同特征来直接判断行为类型,无法对课堂中的交互关系提供更高细粒度的理解。本文提出了一种基于人-物交互(HOI)模型的学生课堂行为检测方法,该方法在对象检测的基础上,进一步利用人-物关系特征来推断交互关系。在本研究中,选择手机作为检测对象与学生进行互动,并对HOI模型进行训练和测试两种类型的行为-使用和不互动。结果表明,训练后的HOI模型在测试中的平均准确率达到83.4%左右,促进了对课堂行为检测更高细粒度的感知和理解,为构建智能课堂和探索个性化教与学路径提供了新的视角。
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