{"title":"Students’ Classroom Behavior Detection Based on Human-Object Interaction Model","authors":"Yonghe Zhang, Wenjiao Qu, Guocheng Zhong, Yundan Xiao","doi":"10.1109/ICSAI57119.2022.10005457","DOIUrl":null,"url":null,"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.","PeriodicalId":339547,"journal":{"name":"2022 8th International Conference on Systems and Informatics (ICSAI)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 8th International Conference on Systems and Informatics (ICSAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSAI57119.2022.10005457","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.