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

<p>This paper proposes a multi-feature fusion approach for action recognition under big data technology with the goal of improving traditional dance video action recognition. By analyzing the basic method of dance action, the extraction process of dance action features is analyzed using both single-layer and hierarchical methods. Multi-feature fusion action recognition is chosen as the main method for action recognition. The image and audio features of the dance video are combined to improve the accuracy of recognizing dance actions. Use the optical flow algorithm to construct a histogram of the optical flow direction. The method’s feasibility is explored by applying the multi-feature fusion recognition method to traditional dance movement recognition. The results show that in traditional dance movement recognition performance, the performance of the method of multi-feature fusion recognition is improved by 7.6% compared to other traditional methods. The multi-feature fusion recognition method has more than 50% accuracy in recognizing different traditional dance movements and similar movements in terms of movement recognition accuracy. To a certain degree, this study enhances the efficiency of traditional dance movement recognition and conserves human and financial resources in dance movement recognition.</p>
</abstract>

This paper proposes a multi-feature fusion approach for action recognition under big data technology with the goal of improving traditional dance video action recognition. By analyzing the basic method of dance action, the extraction process of dance action features is analyzed using both single-layer and hierarchical methods. Multi-feature fusion action recognition is chosen as the main method for action recognition. The image and audio features of the dance video are combined to improve the accuracy of recognizing dance actions. Use the optical flow algorithm to construct a histogram of the optical flow direction. The method’s feasibility is explored by applying the multi-feature fusion recognition method to traditional dance movement recognition. The results show that in traditional dance movement recognition performance, the performance of the method of multi-feature fusion recognition is improved by 7.6% compared to other traditional methods. The multi-feature fusion recognition method has more than 50% accuracy in recognizing different traditional dance movements and similar movements in terms of movement recognition accuracy. To a certain degree, this study enhances the efficiency of traditional dance movement recognition and conserves human and financial resources in dance movement recognition.

Application of big data technology in traditional dance video movement recognition research

Applied Mathematics and Nonlinear Sciences

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

This paper proposes a multi-feature fusion approach for action recognition under big data technology with the goal of improving traditional dance video action...

Application of big data technology in traditional dance video movement recognition research

Publié en ligne: 02 déc. 2023

Pages: -

Reçu: 19 janv. 2023

Accepté: 12 juin 2023

DOI: https://doi.org/10.2478/amns.2023.2.01318

Mots clés
Action recognition, Multi-feature fusion, Image features, Optical flow algorithm, Traditional dance movements

© 2023 Zhenyi Sun, published by Sciendo

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

Application of big data technology in traditional dance video movement recognition research

Publié en ligne: 02 déc. 2023

Pages: -

Reçu: 19 janv. 2023

Accepté: 12 juin 2023

DOI: https://doi.org/10.2478/amns.2023.2.01318

Mots clésAction recognition, Multi-feature fusion, Image features, Optical flow algorithm, Traditional dance movements

© 2023 Zhenyi Sun, published by Sciendo

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

Mots clés
Action recognition, Multi-feature fusion, Image features, Optical flow algorithm, Traditional dance movements