Abstract
Labanotation is one of the well-known notation systems for the documenting and archiving of human motion. It plays a powerful role in dance protection, choreography analysis, and so on. Recently, researchers are committed to using computer technology to automatically generate Labanotation rather than manually drawing. However, the existing generation methods cannot deal with the various changes in motion data, such as different scales, angles, motion modes and limbs. In this paper, we aim to generate Labanotation from motion capture data acquired through real folk dance performances. The main steps include feature extraction, motion segmentation and unit movement analysis. Firstly, a normalized feature named Lie group feature is extracted, which can cope with the challenges of different scales and angles in motion data. Secondly, in order to divide motion with different modes into unit fragments for further recognizing, we propose a segmentation method that combines the speed threshold and the region partition. Thirdly, to generate Laban symbols of unit movements for different limbs, two kinds of neural networks are used for the analysis. On the one hand, LieNet, a powerful network for analyzing time series data based on Lie group structure, is utilized to recognize the lower limb movements. On the other hand, extreme-learning machine, a single hidden layer feedforward neural network, is used to identify the upper limb postures. Experimental results demonstrate that our method of feature extraction, motion segmentation and unit movement analysis achieves better results than the previous works, which makes the generated Labanotation score more reliable.
Highlights
Folk dance is the crystallization of culture, and an important part of intangible cultural heritage
According to the different characteristics of upper and lower limb movements defined in Labanotation, we propose to use LieNet to recognize lower limb movements, and use extreme-learning machine to identify upper limb postures
Our method achieves the best results in the average recognition accuracy of unit movements. This indicates that the proposed neural network architecture, LieNet based on Lie group feature, is suitable for the analysis of the unit movement data
Summary
Folk dance is the crystallization of culture, and an important part of intangible cultural heritage. With the passage of time, many of these dances are facing the risk of transmission loss, so the protection of folk dance has become an urgent task. Dance notation is an effective approach for recording human motion. The generation of dance notation by computer technology is an important way to protect folk dance. When it comes to recording methods of dance movements, people generally think of using intuitive video. The video cannot record the threedimensional human motion completely. Similar to the function of music score, dance score is an appropriate recording method for three-dimensional human motion.
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