Student Action Recognition Based on Deep Convolutional Generative Adversarial Network

Yanyan Cheng; Zhongjian Dai; Ye Ji; Simin Li; Zhiyang Jia; Kaoru Hirota; Yaping Dai

doi:10.1109/CCDC49329.2020.9164040

Student Action Recognition Based on Deep Convolutional Generative Adversarial Network

Yanyan Cheng, Zhongjian Dai, Ye Ji, Simin Li, Zhiyang Jia, Kaoru Hirota, Yaping Dai

School of Automation

Beijing Institute of Technology

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

11 Citations (Scopus)

Abstract

Student action analysis plays an import role in learning and teaching. In order to improve the accuracy of student action recognition in classroom learning, a Deep Convolutional Generative Adversarial Network for Student Action Recognition (DCGANSAR) method is proposed. The method contains two stages: constructing the Deep Convolutional Generative Adversarial Network (DCGAN) to obtain pre-trained weights in the discriminator, and using the discriminator of DCGAN to classify actions. The advantage is that the confrontation between the generator and the discriminator in DCGAN makes the discriminator get stronger, and effective weights are obtained. The pre-trained weights are beneficial for student action recognition, so the accuracy of recognition is improved. Experiments are conducted on the self-built student action dataset. The experimental results demonstrate that the proposed method recognizes student action with high accuracy and fast convergence speed.

Original language	English
Title of host publication	Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	128-133
Number of pages	6
ISBN (Electronic)	9781728158549
DOIs	https://doi.org/10.1109/CCDC49329.2020.9164040
Publication status	Published - Aug 2020
Event	32nd Chinese Control and Decision Conference, CCDC 2020 - Hefei, China Duration: 22 Aug 2020 → 24 Aug 2020

Publication series

Name	Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020

Conference

Conference	32nd Chinese Control and Decision Conference, CCDC 2020
Country/Territory	China
City	Hefei
Period	22/08/20 → 24/08/20

Keywords

Convolutional Network
Generative Adversarial Network (GAN)
Student Action Recognition

Access to Document

10.1109/CCDC49329.2020.9164040

Cite this

Cheng, Y., Dai, Z., Ji, Y., Li, S., Jia, Z., Hirota, K., & Dai, Y. (2020). Student Action Recognition Based on Deep Convolutional Generative Adversarial Network. In Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020 (pp. 128-133). Article 9164040 (Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCDC49329.2020.9164040

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title = "Student Action Recognition Based on Deep Convolutional Generative Adversarial Network",

abstract = "Student action analysis plays an import role in learning and teaching. In order to improve the accuracy of student action recognition in classroom learning, a Deep Convolutional Generative Adversarial Network for Student Action Recognition (DCGANSAR) method is proposed. The method contains two stages: constructing the Deep Convolutional Generative Adversarial Network (DCGAN) to obtain pre-trained weights in the discriminator, and using the discriminator of DCGAN to classify actions. The advantage is that the confrontation between the generator and the discriminator in DCGAN makes the discriminator get stronger, and effective weights are obtained. The pre-trained weights are beneficial for student action recognition, so the accuracy of recognition is improved. Experiments are conducted on the self-built student action dataset. The experimental results demonstrate that the proposed method recognizes student action with high accuracy and fast convergence speed.",

keywords = "Convolutional Network, Generative Adversarial Network (GAN), Student Action Recognition",

author = "Yanyan Cheng and Zhongjian Dai and Ye Ji and Simin Li and Zhiyang Jia and Kaoru Hirota and Yaping Dai",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 32nd Chinese Control and Decision Conference, CCDC 2020 ; Conference date: 22-08-2020 Through 24-08-2020",

year = "2020",

month = aug,

doi = "10.1109/CCDC49329.2020.9164040",

language = "English",

series = "Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020",

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

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address = "United States",

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Cheng, Y, Dai, Z, Ji, Y, Li, S, Jia, Z , Hirota, K & Dai, Y 2020, Student Action Recognition Based on Deep Convolutional Generative Adversarial Network. in Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020., 9164040, Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020, Institute of Electrical and Electronics Engineers Inc., pp. 128-133, 32nd Chinese Control and Decision Conference, CCDC 2020, Hefei, China, 22/08/20. https://doi.org/10.1109/CCDC49329.2020.9164040

Student Action Recognition Based on Deep Convolutional Generative Adversarial Network. / Cheng, Yanyan; Dai, Zhongjian; Ji, Ye et al.
Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 128-133 9164040 (Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020).

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

TY - GEN

T1 - Student Action Recognition Based on Deep Convolutional Generative Adversarial Network

AU - Cheng, Yanyan

AU - Dai, Zhongjian

AU - Ji, Ye

AU - Li, Simin

AU - Jia, Zhiyang

AU - Hirota, Kaoru

AU - Dai, Yaping

PY - 2020/8

Y1 - 2020/8

N2 - Student action analysis plays an import role in learning and teaching. In order to improve the accuracy of student action recognition in classroom learning, a Deep Convolutional Generative Adversarial Network for Student Action Recognition (DCGANSAR) method is proposed. The method contains two stages: constructing the Deep Convolutional Generative Adversarial Network (DCGAN) to obtain pre-trained weights in the discriminator, and using the discriminator of DCGAN to classify actions. The advantage is that the confrontation between the generator and the discriminator in DCGAN makes the discriminator get stronger, and effective weights are obtained. The pre-trained weights are beneficial for student action recognition, so the accuracy of recognition is improved. Experiments are conducted on the self-built student action dataset. The experimental results demonstrate that the proposed method recognizes student action with high accuracy and fast convergence speed.

AB - Student action analysis plays an import role in learning and teaching. In order to improve the accuracy of student action recognition in classroom learning, a Deep Convolutional Generative Adversarial Network for Student Action Recognition (DCGANSAR) method is proposed. The method contains two stages: constructing the Deep Convolutional Generative Adversarial Network (DCGAN) to obtain pre-trained weights in the discriminator, and using the discriminator of DCGAN to classify actions. The advantage is that the confrontation between the generator and the discriminator in DCGAN makes the discriminator get stronger, and effective weights are obtained. The pre-trained weights are beneficial for student action recognition, so the accuracy of recognition is improved. Experiments are conducted on the self-built student action dataset. The experimental results demonstrate that the proposed method recognizes student action with high accuracy and fast convergence speed.

KW - Convolutional Network

KW - Generative Adversarial Network (GAN)

KW - Student Action Recognition

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M3 - Conference contribution

AN - SCOPUS:85091594474

T3 - Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020

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BT - Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020

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ER -

Cheng Y, Dai Z, Ji Y, Li S, Jia Z , Hirota K et al. Student Action Recognition Based on Deep Convolutional Generative Adversarial Network. In Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 128-133. 9164040. (Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020). doi: 10.1109/CCDC49329.2020.9164040

Student Action Recognition Based on Deep Convolutional Generative Adversarial Network

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