A Student Action Recognition Algorithm Based on Adjusted Variational Auto Encoder

Simin Li; Yaping Dai; Ye Ji; Kaoru Hirota; Wei Dai

doi:10.1109/CCDC52312.2021.9601627

A Student Action Recognition Algorithm Based on Adjusted Variational Auto Encoder

Simin Li, Yaping Dai^*, Ye Ji, Kaoru Hirota, Wei Dai

^*Corresponding author for this work

School of Automation

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

Student action recognition plays an important role in detecting students learning behaivor in online courses. In order to improve the accuracy of student action recognition in classroom, an algorithm based on Adjusted Variational Auto Encoder (AVAE) is proposed. By adjusting the traditional Variational Auto Encoder (VAE) method, the proposed algorithm can effectively extract the characteristic parameters of students from classroom video images, and then recognize the students' action. Experiments show that the proposed algorithm for student action recognition preforms better than traditional VAE algorithms with higher accuracy and convergence speed, and improves the recognition accuracy by 5.13% compared with traditional Convolutional Neural Network (CNN) method.

Original language	English
Title of host publication	Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2750-2755
Number of pages	6
ISBN (Electronic)	9781665440899
DOIs	https://doi.org/10.1109/CCDC52312.2021.9601627
Publication status	Published - 2021
Event	33rd Chinese Control and Decision Conference, CCDC 2021 - Kunming, China Duration: 22 May 2021 → 24 May 2021

Publication series

Name	Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021

Conference

Conference	33rd Chinese Control and Decision Conference, CCDC 2021
Country/Territory	China
City	Kunming
Period	22/05/21 → 24/05/21

Keywords

Auto Encoder (AE)
Student Action Recognition
Variational Auto Encoder (VAE)

Access to Document

10.1109/CCDC52312.2021.9601627

Cite this

Li, S., Dai, Y., Ji, Y., Hirota, K., & Dai, W. (2021). A Student Action Recognition Algorithm Based on Adjusted Variational Auto Encoder. In Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021 (pp. 2750-2755). (Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCDC52312.2021.9601627

@inproceedings{425db9aa6354453598161e36269b2480,

title = "A Student Action Recognition Algorithm Based on Adjusted Variational Auto Encoder",

abstract = "Student action recognition plays an important role in detecting students learning behaivor in online courses. In order to improve the accuracy of student action recognition in classroom, an algorithm based on Adjusted Variational Auto Encoder (AVAE) is proposed. By adjusting the traditional Variational Auto Encoder (VAE) method, the proposed algorithm can effectively extract the characteristic parameters of students from classroom video images, and then recognize the students' action. Experiments show that the proposed algorithm for student action recognition preforms better than traditional VAE algorithms with higher accuracy and convergence speed, and improves the recognition accuracy by 5.13% compared with traditional Convolutional Neural Network (CNN) method.",

keywords = "Auto Encoder (AE), Student Action Recognition, Variational Auto Encoder (VAE)",

author = "Simin Li and Yaping Dai and Ye Ji and Kaoru Hirota and Wei Dai",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 33rd Chinese Control and Decision Conference, CCDC 2021 ; Conference date: 22-05-2021 Through 24-05-2021",

year = "2021",

doi = "10.1109/CCDC52312.2021.9601627",

language = "English",

series = "Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "2750--2755",

booktitle = "Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021",

address = "United States",

}

Li, S, Dai, Y, Ji, Y, Hirota, K & Dai, W 2021, A Student Action Recognition Algorithm Based on Adjusted Variational Auto Encoder. in Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021. Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021, Institute of Electrical and Electronics Engineers Inc., pp. 2750-2755, 33rd Chinese Control and Decision Conference, CCDC 2021, Kunming, China, 22/05/21. https://doi.org/10.1109/CCDC52312.2021.9601627

A Student Action Recognition Algorithm Based on Adjusted Variational Auto Encoder. / Li, Simin; Dai, Yaping; Ji, Ye et al.
Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 2750-2755 (Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Student Action Recognition Algorithm Based on Adjusted Variational Auto Encoder

AU - Li, Simin

AU - Dai, Yaping

AU - Ji, Ye

AU - Hirota, Kaoru

AU - Dai, Wei

PY - 2021

Y1 - 2021

N2 - Student action recognition plays an important role in detecting students learning behaivor in online courses. In order to improve the accuracy of student action recognition in classroom, an algorithm based on Adjusted Variational Auto Encoder (AVAE) is proposed. By adjusting the traditional Variational Auto Encoder (VAE) method, the proposed algorithm can effectively extract the characteristic parameters of students from classroom video images, and then recognize the students' action. Experiments show that the proposed algorithm for student action recognition preforms better than traditional VAE algorithms with higher accuracy and convergence speed, and improves the recognition accuracy by 5.13% compared with traditional Convolutional Neural Network (CNN) method.

AB - Student action recognition plays an important role in detecting students learning behaivor in online courses. In order to improve the accuracy of student action recognition in classroom, an algorithm based on Adjusted Variational Auto Encoder (AVAE) is proposed. By adjusting the traditional Variational Auto Encoder (VAE) method, the proposed algorithm can effectively extract the characteristic parameters of students from classroom video images, and then recognize the students' action. Experiments show that the proposed algorithm for student action recognition preforms better than traditional VAE algorithms with higher accuracy and convergence speed, and improves the recognition accuracy by 5.13% compared with traditional Convolutional Neural Network (CNN) method.

KW - Auto Encoder (AE)

KW - Student Action Recognition

KW - Variational Auto Encoder (VAE)

UR - http://www.scopus.com/inward/record.url?scp=85125190288&partnerID=8YFLogxK

U2 - 10.1109/CCDC52312.2021.9601627

DO - 10.1109/CCDC52312.2021.9601627

M3 - Conference contribution

AN - SCOPUS:85125190288

T3 - Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021

SP - 2750

EP - 2755

BT - Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 33rd Chinese Control and Decision Conference, CCDC 2021

Y2 - 22 May 2021 through 24 May 2021

ER -

Li S, Dai Y, Ji Y, Hirota K, Dai W. A Student Action Recognition Algorithm Based on Adjusted Variational Auto Encoder. In Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 2750-2755. (Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021). doi: 10.1109/CCDC52312.2021.9601627

A Student Action Recognition Algorithm Based on Adjusted Variational Auto Encoder

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Keywords

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