Recognition of Adavus in Bharatanatyam Dance

Abstract

Digital heritage has been a challenging problem to preserve the cultural heritage like a dance. Digitization of dance requires systematic analyses and understanding the semantics. However, while several work has been reported in non-Indian Classical Dance (ICD); only few has been done in the ICD because of its complexity. Bharatanatyam is an important ICD form, and Adavus constitute the primary collections of postures and movements in Bharatanatyam. There are 15 classes of Adavus having 52 sub-classes between them. Adavus are the critical building blocks for Bharatanatyam and critical for understanding its artefacts and semantics. Hence, this paper attempts to recognize Adavus based on the sequence of Key Postures (KPs) that define and constitute them. Kinect captures dance performances. The videos are then manually segmented into sequences of Key Frames (KFs) corresponding to the expected KPs. For recognizing KP, we follow two approaches; a) skeleton videos and angles of skeleton bones as features, b) The HOG features from the RGB frames. In both approaches, we train SVMs and recognize the KPs using them. The classifier generated by SVM predicts the sequence of KPs involved in a given Adavu. Since KPs are the string-like encoding symbols of an Adavu, we use the predicted sequence and master sequence of KPs in Edit Distance to recognize the matching Adavu. We use a pre-existing annotated data set of Natta (with eight variants) and Mettu (with four variants) Adavus to achieve over 99% accuracy.