Research on the identification and integration of folk dance creation elements based on big data technology

Abstract

Abstract In order to identify the elements of folk dance creation, two methods of continuous folk dance movement recognition are proposed in this paper. By considering the dance movement patterns as a whole and the templates as incomplete patterns, OE-DTW is applied to match them to segment and recognize each movement pattern one by one. To enhance the performance, a global restriction K-Repetition and endpoint detection condition are proposed to match with OE-DTW, and a penalty-based layer matching algorithm is proposed by using the layer structure feature of SegSVD so that the endpoint of the input movement pattern can be determined by the top layer matching of the latter when matching with the template pattern. Meanwhile, the similarity between two patterns based on this algorithm can also be calculated by the local results obtained from the weighted cumulative layer matching. Experiments show that the average recognition rate of OE-DTW is 0.891, SegSVD is 0.86, and CDP is 0.828. Both methods can effectively deal with the continuous action recognition problem, and they have better recognition results compared with CDP.