Classification of Dance Motions with Depth Cameras Using Subsequence Dynamic Time Warping

Abstract

This paper proposes a method for classifying 3D dance motions especially selected from Korean POP (K-POP) dance performance, which is a key technique for the dance coaching contents and choreography retrieval system. Compared to actions addressed in daily life and existing games, K-POP dance motions are much more dynamic and vary substantially according to the performers. To cope with the variation of the amplitude of pose, we present a practical pose descriptor based on relative rotations between two body joints in the spherical coordinate system. As a method to measure similarity between two incomplete motion sequences, subsequence Dynamic Time Warping (DTW) algorithm is explored that supports partial matches. For the tests, 200 popular dance segments are gathered from 100 K-POP songs by utilizing the Kinect for Windows v2 sensor of Microsoft. The experimental results show that our representation and matching method can achieve an excellent performance in the classification of complex dance motions.