Deparment of Computer Science and Engineering, Anna University, Guindy Campus, Chennai, 600025, India.
Sci Rep. 2024 Apr 4;14(1):7906. doi: 10.1038/s41598-024-58680-w.
This paper delves into the specialized domain of human action recognition, focusing on the Identification of Indian classical dance poses, specifically Bharatanatyam. Within the dance context, a "Karana" embodies a synchronized and harmonious movement encompassing body, hands, and feet, as defined by the Natyashastra. The essence of Karana lies in the amalgamation of nritta hasta (hand movements), sthaana (body postures), and chaari (leg movements). Although numerous, Natyashastra codifies 108 karanas, showcased in the intricate stone carvings adorning the Nataraj temples of Chidambaram, where Lord Shiva's association with these movements is depicted. Automating pose identification in Bharatanatyam poses challenges due to the vast array of variations, encompassing hand and body postures, mudras (hand gestures), facial expressions, and head gestures. To simplify this intricate task, this research employs image processing and automation techniques. The proposed methodology comprises four stages: acquisition and pre-processing of images involving skeletonization and Data Augmentation techniques, feature extraction from images, classification of dance poses using a deep learning network-based convolution neural network model (InceptionResNetV2), and visualization of 3D models through mesh creation from point clouds. The use of advanced technologies, such as the MediaPipe library for body key point detection and deep learning networks, streamlines the identification process. Data augmentation, a pivotal step, expands small datasets, enhancing the model's accuracy. The convolution neural network model showcased its effectiveness in accurately recognizing intricate dance movements, paving the way for streamlined analysis and interpretation. This innovative approach not only simplifies the identification of Bharatanatyam poses but also sets a precedent for enhancing accessibility and efficiency for practitioners and researchers in the Indian classical dance.
本文深入探讨了人类动作识别这一专业领域，聚焦于印度古典舞蹈姿势的识别，特别是 Bharatanatyam 舞蹈。在舞蹈语境中，“Karana” 是指身体、手和脚协调和谐的动作，这是由 Natyashastra 定义的。Karana 的本质在于将 nritta hasta（手部动作）、sthaana（身体姿势）和 chaari（腿部动作）融合在一起。虽然 Natyashastra 规定了 108 种 Karana，但在 Chidambaram 的 Nataraj 寺庙中，我们可以看到精美的石雕上展示了其中的许多动作，这些动作描绘了湿婆与这些动作的关联。在 Bharatanatyam 中，由于手部和身体姿势、mudra（手势）、面部表情和头部动作等方面的变化非常多，因此自动识别姿势是一项具有挑战性的任务。为了简化这个复杂的任务，本研究采用了图像处理和自动化技术。所提出的方法包括四个阶段：涉及骨架化和数据增强技术的图像采集和预处理、从图像中提取特征、使用基于深度学习网络的卷积神经网络模型（InceptionResNetV2）对舞蹈姿势进行分类以及通过从点云中创建网格来可视化 3D 模型。使用先进的技术，如 MediaPipe 库进行身体关键点检测和深度学习网络，简化了识别过程。数据增强是一个关键步骤，它可以扩展小数据集，提高模型的准确性。卷积神经网络模型在准确识别复杂舞蹈动作方面表现出了有效性，为简化分析和解释铺平了道路。这种创新方法不仅简化了 Bharatanatyam 姿势的识别，而且为印度古典舞蹈的从业者和研究人员提供了增强可访问性和效率的范例。
