Recognition of Indian classical dance poses in multi head attention learning framework

The goal of this work is to develop a deep Indian classical dance classifier on online bharatanatyam videos to assist amateurish dance seekers. Previous learning models demonstrated that the global feature representations of dance poses in videos with unpredictable backgrounds have unreliable perfor...

Full description

Saved in:

Bibliographic Details
Published in:	2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE) pp. 1 - 5
Main Authors:	Kishore, P. V. V., Kumar, D. Anil, Moinuddin, SK. Khwaja, Divyasree, L., Kumar, E. Kiran
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-11-2023
Subjects:	Computers in Art Convolutional neural networks Deep Learning Deformation Feature extraction Humanities Indian Classical Dance Knowledge management Magnetic heads Recording Supervised learning
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	The goal of this work is to develop a deep Indian classical dance classifier on online bharatanatyam videos to assist amateurish dance seekers. Previous learning models demonstrated that the global feature representations of dance poses in videos with unpredictable backgrounds have unreliable performance metrics.Therfore, tiny dance datasets with few dancers in controlled environments were used rather than recording from live performances. In this work, the random pixel distributions of the dancer in the online videos with cluttered background are being emphasized using multi frame multi head layer attention (MFMHLA) on deep ResNet features at different resolutions across deep layers. This results in a chronological enhancement of the pose at multiple resolutions across the depth. The experiments were conducted on our online bharatanatyam ICD, BOICDVD22 with 10 songs. The results conclude that the presence of MFMHLA has improved pose feature representations of online dance videos burdened with deformations.
DOI:	10.1109/RMKMATE59243.2023.10369558