کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6941814 | 870621 | 2016 | 51 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Nrityabodha: Towards understanding Indian classical dance using a deep learning approach
ترجمه فارسی عنوان
نیتیتابودا: برای درک رقص کلاسیک هند با استفاده از رویکرد یادگیری عمیق
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کلمات کلیدی
یادگیری عمیق، شبکه عصبی متقاطع، تشخیص ژست، برآورد بدن بدن، کینکت هیستوگرام از گرادیان،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Signal Processing: Image Communication - Volume 47, September 2016, Pages 529-548
Journal: Signal Processing: Image Communication - Volume 47, September 2016, Pages 529-548
نویسندگان
Aparna Mohanty, Pratik Vaishnavi, Prerana Jana, Anubhab Majumdar, Alfaz Ahmed, Trishita Goswami, Rajiv R. Sahay,