Posture and sequence recognition for Bharatanatyam dance performances using machine learning approach

Mallick, Tanwi; Das, Partha Pratim; Majumdar, A. K.

doi:10.48550/arxiv.1909.11023

Cited by 2 publications

(6 citation statements)

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“…The skeletons used in these works are derived ones. So, the ill formed skeleton may affect the classifier in all these approaches, as reported in [17]. However, the RGB is free from these limitations because there is a little chance of getting corrupt RGB data.…”

Section: Motivation and Related Workmentioning

confidence: 99%

“…Identified Gaps: (a) Recognising motions based on skeletal joint velocities as reported in [5], [6] rather than the pattern that varies across a set of frames. Moreover, the ill formed skeleton may affect the classifiers [17] (b) A motion, when played in different instances, does not always contain the same number of frames. However, the earlier approaches [23], [33] assume that all the motion videos contain the same number of frames for making the feature vectors of the same dimension across the motions.…”

Section: Motivation and Related Workmentioning

confidence: 99%

“…To drop the excess frames for the motion samples with more than 16 frames, we formulate an equation as reported in (3). For example: If there exist 20 frames in a given motion then we consider the frame indices (F rame i ) [1,2,3,5,6,7,8,10,11,12,13,15,16,17,18,20]…”

Section: Cnn As Classifiermentioning

confidence: 99%

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Motion Recognition in Bharatanatyam Dance

et al. 2022

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This paper provides a method to understand the underlying semantics of Bharatnatyam dance motion and classifies it. Each dance performance is audio-driven and spans over space and time. The dance is captured and analyzed, which is helpful in cultural heritage preservation, and tutoring systems to assist the naive learner. This paper attempts to solve the fundamental problem; recognizing the motions during a dance performance based on motion-pattern. The used dataset is the video recordings of an Indian Classical Dance form known as Bharatanatyam. The different Adavus (The basic unit of Bharatanatyam) of Bharatanatyam dance are captured using Kinect. We choose RGB from various forms of captured data (RGB, Depth, and Skeleton). Motion History Image (MHI) and Histogram of Gradient of MHI (HoGMHI) are computed for each motion and used as an input for the Machine Learning (ML) algorithms to recognize motion. The paper explores two ML techniques; Support Vector Machine (SVM) and Convolutional Neural Network (CNN). The overall accuracy of both the classifiers is more than 90%. The novelties of the work are (a) analysing all possible involved motions based on the motion-patterns rather than the joint velocities or pose, (b)exploring the impact of training data and the different features on the classifiers' accuracy, (c) not restricting the number of frames in a motion during recognition and formulate a method to deal with the variable number of frames in the motions.

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Section: Motivation and Related Workmentioning

confidence: 99%

Section: Motivation and Related Workmentioning

confidence: 99%

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Motion Recognition in Bharatanatyam Dance

et al. 2022

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“…Capturing and analyzing the dance's multimedia elements can help preserve cultural heritage, creating video referral systems, and helping learners to use practice methods. Mallick et al [4] were tried to solve three basic problems of dance analysis in order to understand the concept of dance forms. Next, identify key poses using machine learning and deep learning approaches.…”

Section: Related Workmentioning

confidence: 99%

“…By the advances in image processing schemes, image processing applications are currently implemented with a computer-assisted system. Gesture recognition is an area that has seen great progress in this field [4]. It is well known that Indian classical dance (ICD) forms like Bharatanatyam, Kathakali and almost 16 types of Indian traditional dances use 51 types of gestures known as mudras.…”

Section: Introductionmentioning

confidence: 99%

Coot optimization based Enhanced Global Pyramid Network for 3D hand pose estimation

Malavath

Devarakonda

2022

Mach. Learn.: Sci. Technol.

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Due to its importance in various applications that need human-computer interaction (HCI), the field of 3D Hand Pose Estimation (HPE) has recently got a lot of attention. The use of technological developments, such as deep learning networks has accelerated the development of reliable 3D HPE systems. Therefore, in this paper, a 3D HPE based on Enhanced Global Pyramid Network (EGPNet) is proposed. Initially, feature extraction is done by backbone model of DetNet with improved EGPNet. The EGPNet is enhanced by the Smish activation function. After the feature extraction, the hand pose estimation is performed based on 3D pose correction network. Additionally, to enhance the estimation performance, Coot optimization algorithm is used to optimize the error between estimated and ground truth hand pose. The effectiveness of the proposed method is experimented on Bharatanatyam, yoga, Kathakali and sign language datasets with different networks in terms of AUC, Median EPE and Mean EPE. The coot optimization is also compared with existing optimization algorithms.

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Posture and sequence recognition for Bharatanatyam dance performances using machine learning approach

Cited by 2 publications

References 0 publications

Motion Recognition in Bharatanatyam Dance

Motion Recognition in Bharatanatyam Dance

Coot optimization based Enhanced Global Pyramid Network for 3D hand pose estimation

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