Bharatnatyam舞蹈中的序列识别
Sequence Recognition in Bharatnatyam dance
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中文总结 AI 辅助
本文提出一种基于CNN和SVM识别Bharatnatyam舞蹈中Adavu序列的方法，利用编辑距离匹配序列，准确率达98%，并测试了所有变体的可扩展性。
中文摘要 AI 辅助
Bharatanatyam是最古老的印度古典舞蹈（ICD），在印度及世界各地被学习和练习。Adavu是这种舞蹈形式的核心。存在15种Adavu和58种变体。每种Adavu变体包含一组定义明确的动作和姿势（称为舞步），这些舞步按特定顺序出现。因此，在学习Adavu时，学生不仅学习舞步，还要注意其出现的顺序。本文提出了一种识别这些序列的方法。在这项工作中，首先，我们分别使用卷积神经网络（CNN）和支持向量机（SVM）识别Adavu中涉及的关键姿势（KPs）和动作。其中，CNN达到99%的准确率，SVM的识别准确率达到84%。接下来，我们将这些KP和动作序列与真实值进行比较，使用编辑距离算法找到最佳匹配，准确率为98%。本文对数字遗产、舞蹈教学系统等领域的现有技术水平做出了巨大贡献。本文解决了三个新颖点：（a）基于KPs和动作识别序列，而非如早期工作仅基于KPs。（b）通过分析每个序列的预测时间来衡量所提出工作的性能。我们还将我们的方法与处理相同问题陈述的先前工作进行比较。（c）通过包含所有Adavu变体来测试所提出方法的可扩展性，这与早期文献仅使用一/两种变体不同。
英文摘要
Bharatanatyam is the oldest Indian Classical Dance (ICD) which is learned and practiced across India and the world. Adavu is the core of this dance form. There exist 15 Adavus and 58 variations. Each Adavu variation comprises a well-defined set of motions and postures (called dance steps) that occur in a particular order. So, while learning Adavus, students not only learn the dance steps but also take care of its sequence of occurrences. This paper proposed a method to recognize these sequences. In this work, firstly, we recognize the involved Key Postures (KPs) and motions in the Adavu using Convolutional Neural Network (CNN) and Support Vector Machine (SVM), respectively. In this, CNN achieves 99% and SVM's recognition accuracy becomes 84%. Next, we compare these KP and motion sequences with the ground truth to find the best match using the Edit Distance algorithm with an accuracy of 98%. The paper contributes hugely to the state-of-the-art in the form of digital heritage, dance tutoring system, and many more. The paper addresses three novelties; (a) Recognizing the sequences based on the KPs and motions rather than only KPs as reported in the earlier works. (b) The performance of the proposed work is measured by analyzing the prediction time per sequence. We also compare our proposed approach with the previous works that deal with the same problem statement. (c) It tests the scalability of the proposed approach by including all the Adavu variations, unlike the earlier literature, which uses only one/two variations.
发表机构
- Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）
机构由 AI 辅助整理，请以论文原文为准。
