A Students' Action Recognition Database In Smart Classroom

With the development of human action recognition, it is possible to automatically recognize students' actions in classroom, providing a new direction for classroom observation in teaching research. Training effective students' action recognition algorithms depends significantly on the qual...

Full description

Saved in:
Bibliographic Details
Published inInternational Conference on Computer Science & Education pp. 523 - 527
Main Authors Li, Xiaomeng, Wang, Min, Zeng, Wei, Lu, Weigang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2019
Subjects
action recognition
Cameras
CNN
Educational technology
Feature extraction
IDT
Oceans
smart classroom
Training
Online AccessGet full text

Cover

More Information
Summary:With the development of human action recognition, it is possible to automatically recognize students' actions in classroom, providing a new direction for classroom observation in teaching research. Training effective students' action recognition algorithms depends significantly on the quality of the action database. However, only a few existing action databases focus on learning environment. In this paper, we contribute to this topic from two aspects. First, a novel students' action recognition database is introduced. The spontaneous action database consists 15 action categories, 817 video clips of 73 students, which are collected in real smart classroom environment. Second, a benchmark experiment was conducted on the database using two kinds of recognition algorithms. The best result is achieved by Inception V3 with 0.9310 accuracy. Such a spontaneous database will help in the development and validation of algorithms for action recognition in learning environment.
ISSN:2473-9464
DOI:10.1109/ICCSE.2019.8845330