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

<p>In an environment where action recognition technology is widely used, dance action recognition technology has also received extensive attention. Due to the complexity of dance movements, there will be a large number of errors in dance movement recognition. Therefore, this paper uses the decision tree algorithm to optimize dance movement recognition. In this paper, the general framework of dance movement recognition is first constructed, and the human body features are extracted and calculated by using the Laban motion analysis method for the changes in the joints of dance movements. Then, use the decision tree algorithm to classify and identify the dance movements, and use it to optimize the self-obscuring motion information repair algorithm to solve the problem of dance movement occlusion and error, and complete the construction of the dance movement recognition model based on CNN network. Empirical analysis shows that the model method's Top-1 and Top-5 accuracy are both 84.7% and 89.32%, respectively. The average probability of incorrect recognition, error recognition, and correct recognition of the optimized dance action recognition in this paper is 8.84%, 3.78 and 87.38 respectively. The average recognition correctness is improved by 12.02%, which indicates that the effect of recognition of the optimized model of dance action masking and error correction in this paper is excellent. It has achieved a better construction in the field of dance action error correction.</p>
</abstract>

In an environment where action recognition technology is widely used, dance action recognition technology has also received extensive attention. Due to the complexity of dance movements, there will be a large number of errors in dance movement recognition. Therefore, this paper uses the decision tree algorithm to optimize dance movement recognition. In this paper, the general framework of dance movement recognition is first constructed, and the human body features are extracted and calculated by using the Laban motion analysis method for the changes in the joints of dance movements. Then, use the decision tree algorithm to classify and identify the dance movements, and use it to optimize the self-obscuring motion information repair algorithm to solve the problem of dance movement occlusion and error, and complete the construction of the dance movement recognition model based on CNN network. Empirical analysis shows that the model method's Top-1 and Top-5 accuracy are both 84.7% and 89.32%, respectively. The average probability of incorrect recognition, error recognition, and correct recognition of the optimized dance action recognition in this paper is 8.84%, 3.78 and 87.38 respectively. The average recognition correctness is improved by 12.02%, which indicates that the effect of recognition of the optimized model of dance action masking and error correction in this paper is excellent. It has achieved a better construction in the field of dance action error correction.

Dance movement error correction based on decision tree algorithm

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"Dance movement error correction based on decision tree algorithm"}

In an environment where action recognition technology is widely used, dance action recognition technology has also received extensive attention. Due to the...

Dance movement error correction based on decision tree algorithm

Online veröffentlicht: 09. Juli 2024

Seitenbereich: -

Eingereicht: 11. März 2024

Akzeptiert: 01. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-1755

Schlüsselwörter
Feature extraction, Decision tree, CNN, Error correction, Dance movement recognition

© 2024 Li Yang., published by Sciendo

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

Dance movement error correction based on decision tree algorithm

Online veröffentlicht: 09. Juli 2024

Seitenbereich: -

Eingereicht: 11. März 2024

Akzeptiert: 01. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-1755

SchlüsselwörterFeature extraction, Decision tree, CNN, Error correction, Dance movement recognition

© 2024 Li Yang., published by Sciendo

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

Schlüsselwörter
Feature extraction, Decision tree, CNN, Error correction, Dance movement recognition