Posture Recognition in Bharathanatyam Images using 2D-CNN
DOI:
https://doi.org/10.56294/dm2023136Keywords:
Bharathanatyam Postures, 2D-CNN, Evaluation MetricsAbstract
The postures are important for conveying emotions, expressing artistic intent, and preserving appropriate technique. Posture recognition in dance is essential for several reasons, as it improving the performance and overall artistic expression of the dancer. The Samapadam, Aramandi, and Muzhumandi are three postures that serve as the foundation for the Bharathanatyam dance style. This work proposes a model designed to recognize the posture portrayed by the dancer. The proposed methodology employs the pre-trained 2D-CNN model fine-tuned using the Bharathanatyam dance image dataset and evaluates the model performance
References
1. Podium School. Bharatanatyam postures for beginners. Disponible en: https://learn.podium.school/bharatanatyam/bharatanatyam-postures-for-beginners/
2. Hu X, Ahuja N. Unsupervised 3D Pose Estimation for Hierarchical Dance Video Recognition. En: 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
3. Wang WJ, Chang JW, Haung SF, Wang RJ. Human Posture Recognition Based on Images Captured by the Kinect Sensor. Int J Adv Robot Syst. 2015.
4. Horta-Martínez LE. 3D printing in the medical field. Seminars in Medical Writing and Education 2022;1:8-8. https://doi.org/10.56294/mw20228.
5. Dantone M, Gall J, Leistner C, Van Gool L. Human Pose Estimation using Body Parts Dependent Joint Regressors. En: IEEE Conference on Computer Vision and Pattern Recognition. 2013.
6. Gupta R, Saini D, Mishra S. Posture detection using Deep Learning for Time Series Data. En: Proceedings of the Third International Conference on Smart Systems and Inventive Technology. 2020.
7. Zhao L, Chen W. Detection and Recognition of Human Body Posture in Motion Based on Sensor Technology. IEEJ Trans Electr Electron Eng. 2019.
8. Ding W, Hu B, Liu H, Wang X, Huang X. Human posture recognition based on multiple features and rule learning. Int J Mach Learn Cybern. 2020.
9. Saha S, Konar A. Topomorphological approach to automatic posture recognition in ballet dance. IET Image Process. 2015.
10. Gautam S, Joshi G, Garg N. Classification of Indian Classical Dance Steps using HOG Features. Int J Adv Res Sci Eng. 2017;6(8).
11. Shubhangi, Tiwary US. Classification of Indian Classical Dance Forms. Springer International Publishing. 2017.
12. Canova-Barrios C, Machuca-Contreras F. Interoperability standards in Health Information Systems: systematic review. Seminars in Medical Writing and Education 2022;1:7-7. https://doi.org/10.56294/mw20227.
13. Mallick T, Das PP, Majumdar AK. Posture and Sequence Recognition For Bharathanatyam dance Performances Using Machine Learning Approach. J Vis Commun Image Represent. 2022;87.
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Copyright (c) 2023 M. Kalaimani , AN. Sigappi (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
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