Skeleton-based automatic generation of Labanotation with neural networks

Xueyan Zhang; Zhenjiang Miao; Qiang Zhang; Jiaji Wang

doi:10.1117/1.JEI.28.2.023026

5 April 2019 Skeleton-based automatic generation of Labanotation with neural networks

Xueyan Zhang, Zhenjiang Miao, Qiang Zhang, Jiaji Wang

Author Affiliations +

Funded by: National Natural Science Foundation of China (NSFC), NSFC, Natural Science Foundation of China, National Key Technology R&D Program of China, National Key Technology RD Program of China

Journal of Electronic Imaging, Vol. 28, Issue 2, 023026 (April 2019). https://doi.org/10.1117/1.JEI.28.2.023026

Abstract

Labanotation is one of the most widely used notation systems and plays a powerful role in recording and archiving traditional folk dance. We propose an end-to-end method for generating Labanotation from motion capture data by identifying the movement of each part of the human body and by assigning corresponding Labanotation symbols. Our method is mainly highlighted in the following aspects: first, we design simple yet highly discriminative skeleton features that can accurately represent human movements; and second, for the recognition of upper limb movements, we adopt fast and efficient extreme-learning neural networks, and for the recognition of lower limb movements, we employ powerful long short-term memory networks. It is worth mentioning that this is the first time that neural networks have been applied to the field of Labanotation generation. Experimental results show that our approach achieves much better recognition accuracy than previous work.

Citation Download Citation

Xueyan Zhang, Zhenjiang Miao, Qiang Zhang, and Jiaji Wang "Skeleton-based automatic generation of Labanotation with neural networks," Journal of Electronic Imaging 28(2), 023026 (5 April 2019). https://doi.org/10.1117/1.JEI.28.2.023026

Received: 14 October 2018; Accepted: 14 March 2019; Published: 5 April 2019

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