Table of Contents Author Guidelines Submit a Manuscript
Advances in Multimedia
Volume 2018 (2018), Article ID 5141402, 10 pages
Research Article

Indian Classical Dance Action Identification and Classification with Convolutional Neural Networks

Department of Electronics and Communication Engineering, KL University, Vaddeswaram, Guntur, India

Correspondence should be addressed to P. V. V. Kishore

Received 13 October 2017; Accepted 20 December 2017; Published 22 January 2018

Academic Editor: Lin Wu

Copyright © 2018 P. V. V. Kishore et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Extracting and recognizing complex human movements from unconstrained online/offline video sequence is a challenging task in computer vision. This paper proposes the classification of Indian classical dance actions using a powerful artificial intelligence tool: convolutional neural networks (CNN). In this work, human action recognition on Indian classical dance videos is performed on recordings from both offline (controlled recording) and online (live performances, YouTube) data. The offline data is created with ten different subjects performing 200 familiar dance mudras/poses from different Indian classical dance forms under various background environments. The online dance data is collected from YouTube for ten different subjects. Each dance pose is occupied for 60 frames or images in a video in both the cases. CNN training is performed with 8 different sample sizes, each consisting of multiple sets of subjects. The remaining 2 samples are used for testing the trained CNN. Different CNN architectures were designed and tested with our data to obtain a better accuracy in recognition. We achieved a 93.33% recognition rate compared to other classifier models reported on the same dataset.