Klasifikasi pose balet dengan mediapipe menggunakan support vector machine

Wiliem, Evelyn (2022) Klasifikasi pose balet dengan mediapipe menggunakan support vector machine. Bachelor thesis, Universitas Pelita Harapan.

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Abstract

Balet telah dianggap sebagai salah satu tarian yang paling sulit dikarenakan postur teknis yang dituntut. Jika dilakukan tanpa bimbingan dapat menyebabkan postur yang buruk pada penari balet dan beberapa cedera serius. Sebuah model dalam mengidentifikasi berbagai pose balet telah dikembangkan dengan kecerdasan buatan untuk mengatasi masalah ini. Tujuan utama dari penelitian ini adalah untuk menunjukkan sebuah metodologi yang menyederhanakan pengenalan pose balet menggunakan framework opensource MediaPipe dan algoritma machine learning Support Vector Machine. Cara kerja model ini akan melewati dua tahap: pertama, mengekstrak titik data dari kumpulan data gambar menggunakan library MediaPipe Pose Estimation, lalu memproses, melatih, memvalidasi, dan menguji data tersebut menggunakan algoritme Support Vector Machine untuk melakukan beberapa klasifikasi pose. Model ini dilatih dalam tujuh pose balet yang berbeda, termasuk First Position, Second Position, Third Position, Fourth Position, Fifth Position, Tendu Devant, and Tendu Derrière. Hal ini dilakukan untuk menilai kompetensi model klasifikasi yang dibuat. Skor akurasi setinggi 87% dicapai dari model klasifikasi pose balet yang dibuat dan telah dikembangkan untuk bekerja input berupa gambar dan video langsung./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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