  Understanding the underlying semantics of performing arts like dance is a
challenging task. Dance is multimedia in nature and spans over time as well as
space. Capturing and analyzing the multimedia content of the dance is useful
for the preservation of cultural heritage, to build video recommendation
systems, to assist learners to use tutoring systems. To develop an application
for 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 of
the dance sequences formed by combining a number of action elements under
certain rules. This paper attempts to solve three fundamental problems of dance
analysis for understanding the underlying semantics of dance forms. Our focus
is on an Indian Classical Dance (ICD) form known as Bharatanatyam. As dance is
driven by music, we use the music as well as motion information for key posture
extraction. Next, we recognize the key postures using machine learning as well
as deep learning techniques. Finally, the dance sequence is recognized using
the Hidden Markov Model (HMM). We capture the multi-modal data of Bharatanatyam
dance using Kinect and build an annotated data set for research in ICD.
