Student Action Recognition Based on Deep Convolutional Generative Adversarial Network

Cheng, Yuehua; Dai, Zhongjian; Ji, Yindong; Li, Simin; Jia, Zhiyang; Hirota, Kaoru

doi:10.1109/ccdc49329.2020.9164040

Cited by 9 publications

(12 citation statements)

References 4 publications

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“…Meanwhile, the proposed algorithm is compared with the methods proposed by other scholars [40][41][42][43][44][45][46][47][48][49] to verify its superiority. For the training, the input image size is set to 432 × 368, the number of cycles is set to 50, the batch size is set to 16, and the initial learning rate is set to 0.001.…”

Section: Discussionmentioning

confidence: 99%

The Fusion Application of Deep Learning Biological Image Visualization Technology and Human-Computer Interaction Intelligent Robot in Dance Movements

Jin

Wen

Xie

2022

Computational Intelligence and Neuroscience

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The paper aims to apply the deep learning-based image visualization technology to extract, recognize, and analyze human skeleton movements and evaluate the effect of the deep learning-based human-computer interaction (HCI) system. Dance education is researched. Firstly, the Visual Geometry Group Network (VGGNet) is optimized using Convolutional Neural Network (CNN). Then, the VGGNet extracts the human skeleton movements in the OpenPose database. Secondly, the Long Short-Term Memory (LSTM) network is optimized and recognizes human skeleton movements. Finally, an HCI system for dance education is designed based on the extraction and recognition methods of human skeleton movements. Results demonstrate that the highest extraction accuracy is 96%, and the average recognition accuracy of different dance movements is stable. The effectiveness of the proposed model is verified. The recognition accuracy of the optimized F-Multiple LSTMs is increased to 88.9%, suitable for recognizing human skeleton movements. The dance education HCI system’s interactive accuracy built by deep learning-based visualization technology reaches 92%; the overall response time is distributed between 5.1 s and 5.9 s. Hence, the proposed model has excellent instantaneity. Therefore, the deep learning-based image visualization technology has enormous potential in human movement recognition, and combining deep learning and HCI plays a significant role.

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Section: Discussionmentioning

confidence: 99%

The Fusion Application of Deep Learning Biological Image Visualization Technology and Human-Computer Interaction Intelligent Robot in Dance Movements

Jin

Wen

Xie

2022

Computational Intelligence and Neuroscience

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“…The use of GANs does not imply the loss of information; on the contrary, adjusted synthetic data can be generated to improve the quality of their relationships, in addition to increasing or reducing anomalous values found in the data sets (Lin, Jain, Wang, Fanti, & Sekar, 2020). At present, along with research emerging from the field of educational technology such as studies of behaviors in virtual learning environments (Cheng et al, 2020;Dorodchi, Al-Hossami, Benedict, & Demeter, 2019) and new studies on academic data for the digital transformation of universities (Bethencourt-Aguilar, Area-Moreira, Sosa-Alonso, & Castellano-Nieves, 2021;Ndou, Ajoodha, & Jadhav, 2020), new information processing procedures and techniques are also needed to guarantee the protection of privacy rights and to hold information about the users, patterns and circumstances of educational agents. The use of Artificial Intelligence in the field of education can solve existing problems regarding samples, treatment and dissemination of data, and is an example of the new methodologies that are being incorporated into education (Bonami et al, 2020).…”

Section: Benefits Of Gans For Educational Researchmentioning

confidence: 99%

Use of Generative Adversarial Networks (GANs) in Educational Technology Research

Aguilar

Castellanos–Nieves

Alonso

et al. 2023

J. New Approaches Educ. Res.

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In the context of Artificial Intelligence, Generative Adversarial Nets (GANs) allow the creation and reproduction of artificial data from real datasets. The aims of this work are to seek to verify the equivalence of synthetic data with real data and to verify the possibilities of GAN in educational research. The research methodology begins with the creation of a survey that collects data related to the self-perceptions of university teachers regarding their digital competence and technological-pedagogical knowledge of the content (TPACK model). Once the original dataset is generated, twenty-nine different synthetic samples are created (with an increasing N) using the COPULA-GAN procedure. Finally, a two-stage cluster analysis is applied to verify the interchangeability of the synthetic samples with the original, in addition to extracting descriptive data of the distribution characteristics, thereby checking the similarity of the qualitative results. In the results, qualitatively very similar cluster structures have been obtained in the 150 tests carried out, with a clear tendency to identify three types of teaching profiles, based on their level of technical-pedagogical knowledge of the content. It is concluded that the use of synthetic samples is an interesting way of improving data quality, both for security and anonymization and for increasing sample sizes.

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“…DCGAN is a deep convolutional generative adversarial network, which is composed of generative network and a discriminant network [13]. Compared with the GAN model, DCGAN optimizes network structure using the concept of a deep convolution network, to improve the quality of sample generation and convergence speed.…”

Section: A Improve the Dcgan Networkmentioning

confidence: 99%

“…The dataset images are then randomly divided into two groups: 80% of the dataset is used for parameter learning and network training, whereas the other 20% is used to test the generalization and recognition ability of the model, and the two datasets do not intersect each other. (15,30), (13,48), (19,38), (17,59), (14,81), (17,75), (24,55), (19,100), (14,144). The distribution is shown in Table 2.…”

Section: A Preparation Of Adhesive Structures Defect Datasetmentioning

confidence: 99%

Defect Identification of Adhesive Structure Based on DCGAN and YOLOv5

et al. 2022

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To overcome the problem of small defect samples and the imbalanced distribution of defect categories during adhesive structure defect detection, a defect identification approach based on DCGAN and YOLOv5 is proposed. The above problems are solved by fine-tuning the structure and the loss function of the DCGAN, generating high-quality defect images, and expanding the dataset of adhesive structure defects. Generally, using the EIOU loss function in the YOLOv5 network allows the network to converge faster during training and improve the recognition effect. In this article, it is illustrated by comparing with the GIOU loss function, that utilizing the EIOU loss function increases the mAP value by 3.9% and the recall by 10.5%, but the precision decreases. To solve this problem, the feature extraction capability of the network is enhanced by incorporating the CBAM after the C3 module in the YOLOv5 network. Interestingly, the mAP, precision, and recall of the optimized YOLOv5 algorithm are improved to 78.6%, 77.2%, and 76%, respectively, where the precision compared to the original model improved by 10.6%. This study has demonstrated that the improved YOLOv5 model can effectively detect adhesive structure defects, which provides defect identification and control theoretical research and technical support.

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Student Action Recognition Based on Deep Convolutional Generative Adversarial Network

Cited by 9 publications

References 4 publications

The Fusion Application of Deep Learning Biological Image Visualization Technology and Human-Computer Interaction Intelligent Robot in Dance Movements

The Fusion Application of Deep Learning Biological Image Visualization Technology and Human-Computer Interaction Intelligent Robot in Dance Movements

Use of Generative Adversarial Networks (GANs) in Educational Technology Research

Defect Identification of Adhesive Structure Based on DCGAN and YOLOv5

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