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Understanding the underlying semantics of performing arts like dance is achallenging task. Dance is multimedia in nature and spans over time as well asspace. Capturing and analyzing the multimedia content of the dance is usefulfor the preservation of cultural heritage, to build video recommendationsystems, to assist learners to use tutoring systems. To develop an applicationfor dance, three aspects of dance analysis need to be addressed: 1)Segmentation of the dance video to find the representative action elements, 2)Matching or recognition of the detected action elements, and 3) Recognition ofthe dance sequences formed by combining a number of action elements undercertain rules. This paper attempts to solve three fundamental problems of danceanalysis for understanding the underlying semantics of dance forms. Our focusis on an Indian Classical Dance (ICD) form known as Bharatanatyam. As dance isdriven by music, we use the music as well as motion information for key postureextraction. Next, we recognize the key postures using machine learning as wellas deep learning techniques. Finally, the dance sequence is recognized usingthe Hidden Markov Model (HMM). We capture the multi-modal data of Bharatanatyamdance using Kinect and build an annotated data set for research in ICD.

Posture and sequence recognition for Bharatanatyam dance performances using machine learning approach