scholarly journals Indian Classical Dance Classification with Adaboost Multiclass Classifier on Multifeature Fusion

2017 ◽  
Vol 2017 ◽  
pp. 1-18 ◽  
Author(s):  
K. V. V. Kumar ◽  
P. V. V. Kishore ◽  
D. Anil Kumar
Keyword(s):  
Feature Fusion ◽  
Discrete Wavelet ◽  
Classical Dance ◽  
Video Frames ◽  
Shape Signature ◽  
Human Movements ◽  
Hu Moments ◽  
Interesting Task ◽  
Indian Classical Dance ◽  
Multiclass Classifier

Extracting and recognizing complex human movements from unconstraint online video sequence is an interesting task. In this paper the complicated problem from the class is approached using unconstraint video sequences belonging to Indian classical dance forms. A new segmentation model is developed using discrete wavelet transform and local binary pattern (LBP) features for segmentation. A 2D point cloud is created from the local human shape changes in subsequent video frames. The classifier is fed with 5 types of features calculated from Zernike moments, Hu moments, shape signature, LBP features, and Haar features. We also explore multiple feature fusion models with early fusion during segmentation stage and late fusion after segmentation for improving the classification process. The extracted features input the Adaboost multiclass classifier with labels from the corresponding song (tala). We test the classifier on online dance videos and on an Indian classical dance dataset prepared in our lab. The algorithms were tested for accuracy and correctness in identifying the dance postures.

scholarly journals Indian classical dance action identification using adaptive graph matching from unconstrained videos

2017 ◽  
Vol 7 (1.1) ◽  
pp. 500
Author(s):  
K V.V. Kumar ◽  
P V.V. Kishore
Keyword(s):  
Local Binary Pattern ◽  
Graph Matching ◽  
Feature Fusion ◽  
Discrete Wavelet ◽  
Action Identification ◽  
Classical Dance ◽  
Human Movements ◽  
Multiple Feature ◽  
Multiple Feature Fusion ◽  
Indian Classical Dance

Extracting and recognizing complex human movements from unconstraint online video sequence is a challenging task. In this work the problem becomes complicated by the use of unconstraint video sequences belonging to Indian classical dance forms. A new segmentation model is developed using discrete wavelet transform and local binary pattern features for segmentation. We also explore multiple feature fusion models with early fusion and late fusion techniques for improving the classification process. The extracted features were represented as a graph and a novel adaptive graph matching algorithm is proposed. We test the algorithms on online dance videos and on an Indian classical dance dataset prepared in our lab. The algorithms were tested for accuracy and correctness in identifying the dance postures.

scholarly journals Indian Classical Dance Action Identification and Classification with Convolutional Neural Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
P. V. V. Kishore ◽  
K. V. V. Kumar ◽  
E. Kiran Kumar ◽  
A. S. C. S. Sastry ◽  
M. Teja Kiran ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Action Identification ◽  
Classical Dance ◽  
Human Movements ◽  
Indian Classical Dance

Extracting and recognizing complex human movements from unconstrained online/offline video sequence is a challenging task in computer vision. This paper proposes the classification of Indian classical dance actions using a powerful artificial intelligence tool: convolutional neural networks (CNN). In this work, human action recognition on Indian classical dance videos is performed on recordings from both offline (controlled recording) and online (live performances, YouTube) data. The offline data is created with ten different subjects performing 200 familiar dance mudras/poses from different Indian classical dance forms under various background environments. The online dance data is collected from YouTube for ten different subjects. Each dance pose is occupied for 60 frames or images in a video in both the cases. CNN training is performed with 8 different sample sizes, each consisting of multiple sets of subjects. The remaining 2 samples are used for testing the trained CNN. Different CNN architectures were designed and tested with our data to obtain a better accuracy in recognition. We achieved a 93.33% recognition rate compared to other classifier models reported on the same dataset.

A Comparative Study of Certain Classifiers for Bharatanatyam Mudra Images' Classification using Hu-Moments

2019 ◽  
Vol 8 (2) ◽  
pp. 58-71
Author(s):  
Basavaraj S Anami ◽  
Venkatesh Arjunasa Bhandage
Keyword(s):  
Comparative Study ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Classical Dance ◽  
Dance Teachers ◽  
Hu Moments ◽  
E Learning ◽  
Indian Classical Dance ◽  
Nearest Neighbor Classifiers

India is rich in culture and heritage where various traditional dances are practiced. Bharatanatyam is an Indian classical dance, which is composed of various body postures and hand gestures. This ancient art of dance has to be studied under guidance of dance teachers. In present days there is a scarcity of Bharatanatyam dance teachers. There is a need to adopt technology to popularize this dance form. This article presents a 3-stage methodology for the classification of Bharatanatyam mudras. In the first stage, acquired images of Bharatanatyam mudras are preprocessed to obtain contours of mudras using canny edge detector. In the second stage, Hu-moments are extracted as features. In the third stage, rule-based classifiers, artificial neural networks, and k-nearest neighbor classifiers are used for the classification of unknown mudras. The comparative study of classification accuracies of classifiers is provided at the end. The work finds application in e-learning of ‘Bharatanatyam' dance in particular and dances in general and automation of commentary during concerts.

Survey of Musculoskeletal Injuries among Female Bharatanatyam Dancers in the Udupi District of India

2021 ◽  
Vol 36 (3) ◽  
pp. 199-206
Author(s):  
Lavanya P Kumar ◽  
Shruti J Shenoy
Keyword(s):  
Medical Professional ◽  
Musculoskeletal Injuries ◽  
Limited Information ◽  
Classical Dance ◽  
Dance Training ◽  
Lower Back ◽  
Injury Occurrence ◽  
Indian Classical Dance ◽  
The Impact ◽  
And Training

BACKGROUND: Bharatanatyam is an Indian classical dance form that is practiced globally. There is limited information about the prevalence of injuries in Bharatanatyam dancers. OBJECTIVES: To investigate the prevalence of musculoskeletal injuries and specifics of dance training in female Bharatanatyam dancers in the Udupi district of India. METHODS: We developed and tested a survey for Bharatanatyam dancers regarding injury history in the prior year, including location, time loss, cause, and need for medical help. We also obtained demographic and training information. RESULTS: 101 dancers completed the survey. 10.8% of dancers reported musculoskeletal injuries because of participation in dance. They sustained 0.65 injuries/1,000 hours of dancing. The most frequently injured areas were ankle (27.2%) and knee (27.2%) followed by lower back (13.6%) and hip (9%). Despite being injured, 36.4% of the dancers continued to dance. 54.5% of the injured dancers sought the help of a medical professional for their dance-related injuries. The most common surface for dance was concrete followed by other hard surfaces such as marble and tile. CONCLUSION: Female Bharatanatyam dancers are prone to injuries of the lower extremity and back. Most dancers in our study practice the Pandanalluru style on hard surfaces. There is a need to investigate the impact of training factors on the injury occurrence.

Technological Mediation in Odissi Dance

2015 ◽  
pp. 474-491
Author(s):  
Shreelina Ghosh
Keyword(s):  
Qualitative Research ◽  
Digital Technologies ◽  
Classroom Community ◽  
Traditional Values ◽  
Online Instructors ◽  
Classical Dance ◽  
Dance Teachers ◽  
Online Videos ◽  
Virtual Class ◽  
Indian Classical Dance

The practice of teaching in an online composition class might potentially eliminate interpersonal interactivity in a classroom community. Digital mediation can be problematic for functional collaboration in a virtual class. The problem that online instructors might face is one that some traditional Odissi dance teachers also experience. In order to explore the conflict between tradition and mediations with technology, this study focuses on Odissi, an Indian classical dance, and examines how digital technologies of teaching, like CDs, DVD, online videos, and synchronous videos, are transforming the practice and teaching of this traditional dance. A qualitative research of the field of Odissi dance revealed that technologizing the dance might be unavoidable, but to some practitioners it may be disrupting Odissi's traditional values. This chapter reasserts the position of the teacher in an online pedagogic space and argues that the presence or simulated presence of bodies might be vital in learning and composing collaboratively.

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