Computer Vision for the Ballet Industry: A Comparative Study of Methods for Pose Recognition

Fourie, Margaux; Haar, Dustin van der

doi:10.1007/978-3-030-53337-3_9

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…Hal ini dikarenakan tarian balet sendiri sudah dikenal akan sebuah bentuk seni yang sangat sulit untuk dicapai [1]. Untuk mengatasi hal tersebut, sangat penting adanya bimbingan dan perhatian satu per satu oleh guru saat dikelas dan melakukan latiha n tambahan dengan sendirinya [2]. Tetapi tanpa bimbingan guru, penari memiliki kemungkinan besar tidak dapat melakukan pose dengan benar walaupun sudah melihat video gerakan balet.…”

Section: Pendahuluanunclassified

Implementasi Algoritma Support Vector Machine Terhadap Klasifikasi Pose Balet

Romindo

Barus

Pangaribuan

et al. 2022

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Ballet is considered as one of the most difficult dance due to its technical posture demanded. If performed without guidance it may cause bad posture to ballerina and some serious injuries. A model in identifying different ballet poses is developed with artificial intelligence in order to tear down this barrier. The main purpose of this paper is to demonstrate a methodology that simplified Ballet Pose Recognition using an opensource framework called MediaPipe and a machine learning algorithm called Support Vector Machine. How the model work is it will pass through two stages: first, it extracts data points from an image dataset using the MediaPipe Pose Estimation library, and then it preprocesses the data, trains, validates, and tests it using the Support Vector Machine algorithm to do some pose classification. The model is trained in seven distinct ballet poses, including First Position, Second Position, Third Position, Fourth Position, Fifth Position, Tendu Devant, and Tendu Derrière. This is purposely done in order to assess the competence of the classification model. An accuracy score of 87% is achieved from the ballet pose classification model and is developed to work on images and live videos.

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Section: Pendahuluanunclassified

Implementasi Algoritma Support Vector Machine Terhadap Klasifikasi Pose Balet

Romindo

Barus

Pangaribuan

et al. 2022

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“…Human Pose Estimation (HPE) is an essential computer vision activity highly preferred in the research domain. It is also performed in various applications like recognition of activities, humanity, movies, animation, the interaction between humans and computers, and so on (Fourie, 2020& Jain, 2020.…”

Section: Human Pose Estimationmentioning

confidence: 99%

The Analysis of the Impact of Yoga on Healthcare and Conventional Strategies for Human Pose Recognition

Vallabhaneni¹

2021

TURCOMAT

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Human pose estimation is a profound, established computer vision issue that has uncovered numerous past difficulties. Breaking down human exercise is advantageous in multiple fields like surveillance, biometrics, and many healthcare applications. Workout with yoga poses is famous these days since yoga activities can expand adaptability and muscular quality, and the respiration procedure will be improvised. The yoga postures evaluation is hard to check, so specialists will most likely be unable to benefit from the exercises ultimately. IoT-based yoga frameworks are required for individuals who need to rehearse Yoga at home. A few studies are recommended camera-oriented or wearable gadget-oriented yoga posture finding strategies with more precision. Nonetheless, camera-based plans have security and privacy issues, and the wearable device-based methods are illogical in the earlier applications. To build such systems, one must have a strong foundation and current research in pose estimation. In this paper, first, the impact of Yoga on humans with various stress levels is analysed on the real-time data. Second, the comprehensive review of yoga posture recognition systems from machine learning to deep learning strategies and evaluation metrics discussed

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A Feature Importance Study in Ballet Pose Recognition with OpenPose

Fourie

Haar

2020

Artificial Intelligence in HCI

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Computer Vision for the Ballet Industry: A Comparative Study of Methods for Pose Recognition

Cited by 5 publications

References 15 publications

Implementasi Algoritma Support Vector Machine Terhadap Klasifikasi Pose Balet

Implementasi Algoritma Support Vector Machine Terhadap Klasifikasi Pose Balet

The Analysis of the Impact of Yoga on Healthcare and Conventional Strategies for Human Pose Recognition

A Feature Importance Study in Ballet Pose Recognition with OpenPose

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