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Big Data Technologies and Applications. 11th and 12th EAI International Conference, BDTA 2021 and BDTA 2022, Virtual Event, December 2021 and 2022, Proceedings

Research Article

Pose+Context: A Model for Recognizing Non-verbal Teaching Behavior of Normal College Student

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-33614-0_17,
        author={Yonghe Zhang and Bing Li and Xiaoming Cao},
        title={Pose+Context: A Model for Recognizing Non-verbal Teaching Behavior of Normal College Student},
        proceedings={Big Data Technologies and Applications. 11th and 12th EAI International Conference, BDTA 2021 and BDTA 2022, Virtual Event, December 2021 and 2022, Proceedings},
        proceedings_a={BDTA},
        year={2023},
        month={5},
        keywords={deep learning computer vision pose estimation classroom behavior analysis},
        doi={10.1007/978-3-031-33614-0_17}
    }
    
  • Yonghe Zhang
    Bing Li
    Xiaoming Cao
    Year: 2023
    Pose+Context: A Model for Recognizing Non-verbal Teaching Behavior of Normal College Student
    BDTA
    Springer
    DOI: 10.1007/978-3-031-33614-0_17
Yonghe Zhang1, Bing Li1,*, Xiaoming Cao1
  • 1: Normal College, Shenzhen University
*Contact email: libingice@szu.edu.cn

Abstract

Normal college students practice their teaching skills to prepare for their teacher career. However, due to the complexity of teaching skills and the overburdening of instructors, their needs for instructional feedback are often not met. To server automatic feedbacks to normal college students, this paper proposes a deep learning model, called Pose+Contex, to recognize three types of non-verbal teaching behaviors (NVTB). The model includes three parts: (1) context detection, (2) pose estimation, and (3) behavior recognition. Our model is featured by the context detection component, and experiments show that it performs better than a similar model without this component.

Keywords
deep learning computer vision pose estimation classroom behavior analysis
Published
2023-05-26
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-33614-0_17
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