Indian Classical Dance Action Identification and Classification with Convolutional Neural Networks

TLDR

Extracting and recognizing complex human movements from unconstrained online/offline video sequences remains a challenging computer‑vision problem. The study proposes classifying Indian classical dance actions using convolutional neural networks. The authors trained and tested multiple CNN architectures on a dataset of 200 dance poses from 10 subjects recorded both offline and online, using 60‑frame clips and varying sample sizes. The proposed CNN models achieved a 93.33 % recognition rate, outperforming other classifiers on the same dataset.

Abstract

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.