<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Usability of QR Code in Augmented Reality system has been used for digital content accessible publicly. However, QR Code in AR system still has imprecision tracking. In this article we propose merging QR Code within conventional marker and backpropagation neural network (BPNN) algorithm to recognizing QR Code Finder Pattern. The method which our chosen to approaching conventional marker. The result of BPNN testing, QRFP detected in perspective distortion with ID-encoded character length 78, 53 and 32. The result has accuracy of 6DOF ±10.65° pitching, ±15.03° yawing and ±408.07 surging, marker stability has 97.625% and computation time runs at 35.41 fps.</p></abstract>

Usability of QR Code in Augmented Reality system has been used for digital content accessible publicly. However, QR Code in AR system still has imprecision tracking. In this article we propose merging QR Code within conventional marker and backpropagation neural network (BPNN) algorithm to recognizing QR Code Finder Pattern. The method which our chosen to approaching conventional marker. The result of BPNN testing, QRFP detected in perspective distortion with ID-encoded character length 78, 53 and 32. The result has accuracy of 6DOF ±10.65° pitching, ±15.03° yawing and ±408.07 surging, marker stability has 97.625% and computation time runs at 35.41 fps.

Applying Merging Convetional Marker and Backpropagation Neural Network in QR Code Augmented Reality Tracking

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Usability of QR Code in Augmented Reality system has been used for digital content accessible publicly. However, QR Code in AR system still has imprecision...

Applying Merging Convetional Marker and Backpropagation Neural Network in QR Code Augmented Reality Tracking

Publié en ligne: 16 déc. 2013

Pages: 1918 - 1948

Reçu: 21 juin 2013

Accepté: 30 sept. 2013

DOI: https://doi.org/10.21307/ijssis-2017-620

© 2013 Gia M. Agusta et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.