<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, we first analyze full domain convolution and LSTM to evaluate human pose by convolutional neural network and LSTM network. Secondly, graph structure skeleton image and skeleton point image classifier based on CNN and LSTM is constructed. The two-dimensional pose assessment method and three-dimensional pose assessment method were used to empirically analyze the human pose assessment. The results show that the average accuracy mAP values of the traditional evaluation methods are 69.7, 72.3, 71.4, and 74.4, respectively, while the average accuracy mAP value of the method used for 2D pose evaluation is 74.6. Where the average error of LReLU is the smallest. This shows that full-domain convolution and LSTM can be effective for human pose evaluation.</p>
</abstract>

In this paper, we first analyze full domain convolution and LSTM to evaluate human pose by convolutional neural network and LSTM network. Secondly, graph structure skeleton image and skeleton point image classifier based on CNN and LSTM is constructed. The two-dimensional pose assessment method and three-dimensional pose assessment method were used to empirically analyze the human pose assessment. The results show that the average accuracy mAP values of the traditional evaluation methods are 69.7, 72.3, 71.4, and 74.4, respectively, while the average accuracy mAP value of the method used for 2D pose evaluation is 74.6. Where the average error of LReLU is the smallest. This shows that full-domain convolution and LSTM can be effective for human pose evaluation.

Human pose evaluation based on full-domain convolution and LSTM

Nanjing University of Information Science & Technology

Communication University of China Nanjing

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Human pose evaluation based on full-domain convolution and LSTM"}

In this paper, we first analyze full domain convolution and LSTM to evaluate human pose by convolutional neural network and LSTM network. Secondly, graph...

Human pose evaluation based on full-domain convolution and LSTM

Online veröffentlicht: 17. Okt. 2023

Seitenbereich: -

Eingereicht: 26. Dez. 2022

Akzeptiert: 27. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00680

Schlüsselwörter
Full domain convolution, LSTM, Bone structure skeleton, Human pose assessment, Convolutional neural network

© 2023 Yu Zou et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Human pose evaluation based on full-domain convolution and LSTM

Online veröffentlicht: 17. Okt. 2023

Seitenbereich: -

Eingereicht: 26. Dez. 2022

Akzeptiert: 27. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00680

SchlüsselwörterFull domain convolution, LSTM, Bone structure skeleton, Human pose assessment, Convolutional neural network

© 2023 Yu Zou et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Full domain convolution, LSTM, Bone structure skeleton, Human pose assessment, Convolutional neural network