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Proceedings Paper

BNU-LCSAD: a video database for classroom student action recognition
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Paper Abstract

With the development and application of digital cameras, especially in education, a great number of digital video recordings are produced in classrooms. Taking Beijing Normal University as an example, 3.4 TB of videos are recorded every day in more than 200 classrooms. Such huge data is beneficial for us, computer vision researchers, to automatically recognize students' classroom actions and even evaluate the quality of classroom teaching. To focus action recognition on students, we propose Beijing Normal University Large-scale Classroom Student Action Database version 1.0(BNU-LCSAD) which is the first large-scale classroom student action database for student action recognition and consists of 10 classroom student action classes from digital camera recordings at BNU. We introduce the construct and label Processing of this database in detail. In Addition , we provide baseline of student action recognition results based our new database using C3D network.

Paper Details

Date Published: 18 November 2019
PDF: 8 pages
Proc. SPIE 11187, Optoelectronic Imaging and Multimedia Technology VI, 111871V (18 November 2019); doi: 10.1117/12.2539052
Show Author Affiliations
Bo Sun, Beijing Normal Univ. (China)
Kaijie Zhao, Beijing Normal Univ. (China)
Yongkang Xiao, Beijing Normal Univ. (China)
Jun He, Beijing Normal Univ. (China)
Lejun Yu, Beijing Normal Univ. (China)
Yong Wu, Beijing Normal Univ. (China)
Huanqing Yan, Beijing Normal Univ. (China)

Published in SPIE Proceedings Vol. 11187:
Optoelectronic Imaging and Multimedia Technology VI
Qionghai Dai; Tsutomu Shimura; Zhenrong Zheng, Editor(s)

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