OPTIMASI HAND LANDMARK PADA HAND TRACKING MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK SEBAGAI AUGMENTED REALITY GAME CONTROLL

SIREGAR, TEGAR CAHYA BAYU and Fachrurrozi, Muhammad and Primanita, Anggina (2023) OPTIMASI HAND LANDMARK PADA HAND TRACKING MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK SEBAGAI AUGMENTED REALITY GAME CONTROLL. Undergraduate thesis, Sriwijaya University.

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Abstract

This study focuses on creating a flexible and lightweight solution of hand landmark prediction using Convolutional Neural Network. Beyond replicationg high-end VR/AR headsets’ hand tracking capabilities, the motivation extends to promoting gamer’s physical activity using augmented reality experiences. By using the hand landmark prediction as game controller for in-game actions, that encourages gamers to move their bodies, fostering a healthier and more active gaming experience. To achieve that, this study explores 3 configuration set of keypoint 11, 17, and 21 for maximizing the accuracy and speed in hand tracking. In addition, the methodology involves building an affordable and lightweight architecture based on U-Net. U-Net structure adapted to use Hourglass Network Block with depthwise convolutional layers and embedded pre and post processing layer. From the experiment, the model got 0.061 Mean Per Joint Position Error@128px and 14 to 16 dynamic frame rate score in 21 keypoint with low hardware i3 gen 7 CPU paired with MX130 GPU laptop. Keywords : Keypoint Prediction, Augmented Reality, Convolutional Neural Network (CNN), U-Net

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Keypoint Prediction, Augmented Reality, Convolutional Neural Network (CNN), U-Net
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Tegar Cahya Bayu Siregar
Date Deposited:	19 Jan 2024 08:44
Last Modified:	19 Jan 2024 08:44
URI:	http://repository.unsri.ac.id/id/eprint/139024

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