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Motion Classification in Bharatanatyam Dance

2020; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-981-15-8697-2_38

1865-0937

Himadri Bhuyan, Mousam Roy, Partha Pratim Das,

Human Motion and Animation

The paper presents a method to classify the unique motions in Bharatanatyam dance videos. Unlike the motions in our daily activities, the motions involved in the dance is rather complex in nature. Looking at the state of art, there is a new scope of the motion classification in the domain of dance. During dance performance, the number of frames in each motion may vary, which leads to the variable feature lengths. This variability, makes comparisons of motions difficult for classification and adds to challenges of the current work. We use the velocities of the skeleton joints as a feature. The joint coordinates are captured by Kinect 1.0. Dynamic Time Warping (DTW) and kNN algorithm are used for classification. The DTW is used to measure the similarity between two motions using skeleton joint velocities and the extracted similarity measure is supplied to the kNN algorithm to identify similar motions. The paper adopts two techniques while measuring the similarity of the joint velocities; i) Non-Weighted Joints ii) Weighted Joints. To optimize the joint weights, Particle Swarm Optimization (PSO) algorithm is used. We also compare the result of the two techniques and highlight the pros and cons of each. The proposed approach is simple and very effective and eventually achieves an accuracy of more than 85%. Finally motion classification in Indian Classical Dance (ICD) can help in digital heritage, design of dance tutoring system, dance synthesis application, and the like.