Nrityabodha: Towards understanding Indian classical dance using a deep learning approach
| Autor: | Trishita Goswami, Alfaz Ahmed, Anubhab Majumdar, Pratik Vaishnavi, Prerana Jana, Aparna Mohanty, Rajiv R. Sahay |
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| Rok vydání: | 2016 |
| Předmět: |
Dance
business.industry Computer science Deep learning 020207 software engineering 02 engineering and technology Machine learning computer.software_genre Convolutional neural network Human–computer interaction Gesture recognition Signal Processing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Meaning (existential) Artificial intelligence Electrical and Electronic Engineering Performing arts Transfer of learning business computer Software Gesture |
| Zdroj: | Signal Processing: Image Communication. 47:529-548 |
| ISSN: | 0923-5965 |
| DOI: | 10.1016/j.image.2016.05.019 |
| Popis: | Indian classical dance has existed since over 5000 years and is widely practised and performed all over the world. However, the semantic meaning of the dance gestures and body postures as well as the intricate steps accompanied by music and recital of poems is only understood fully by the connoisseur. The common masses who watch a concert rarely appreciate or understand the ideas conveyed by the dancer. Can machine learning algorithms aid a novice to understand the semantic intricacies being expertly conveyed by the dancer? In this work, we aim to address this highly challenging problem and propose deep learning based algorithms to identify body postures and hand gestures in order to comprehend the intended meaning of the dance performance. Specifically, we propose a convolutional neural network and validate its performance on standard datasets for poses and hand gestures as well as on constrained and real-world datasets of classical dance. We use transfer learning to show that the pre-trained deep networks can reduce the time taken during training and also improve accuracy. Interestingly, we show with experiments performed using Kinect in constrained laboratory settings and data from Youtube that it is possible to identify body poses and hand gestures of the performer to understand the semantic meaning of the enacted dance piece. |
| Databáze: | OpenAIRE |
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