NDLI: Markerless tracking for augmented reality applied in reconstruction of Yuanmingyuan archaeological site

Content Provider	IEEE Xplore Digital Library
Author	Junwei Guo Yongtian Wang Jing Chen Jingdun Lin Lun Wu Kang Xue Wei Liu Jiangen Zhang
Copyright Year	2009
Description	Author affiliation: School of Optolelectronics, Beijing Institute of Technology, 100081, China (Junwei Guo; Yongtian Wang; Jing Chen; Jingdun Lin; Lun Wu; Kang Xue; Wei Liu; Jiangen Zhang)
Abstract	This paper presents an algorithm based on the method of supervised machine learning and multi-keyframes to achieve markerless augmented reality (AR) application when there is a locally planar object in the scene. The main goal is to solve the problem of AR tracking in outdoor environment by only using vision and natural features. Instead of tracking fiducial markers, we track natural keypoints, during which the point correspondences are established from the classification perspective. The tracking range is able to be extended by employing many reference images. These results in a promising algorithm that successfully tested on a touring guide system, which provides views of virtual original appearance superimposed to ruins of Yuanmingyuan archaeological site of China. Comparisons are also made between ARToolkit and the proposed algorithm in indoor environment. Experimental results demonstrated that our algorithm is characterized by fairly robustness and high time efficiency in both indoor and outdoor application.
Starting Page	324
Ending Page	329
File Size	923024
Page Count	6
File Format	PDF
ISBN	9781424436996
DOI	10.1109/CADCG.2009.5246882
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	System testing Machine learning algorithms Layout Indoor environments Machine learning Robustness Intelligent systems Augmented reality Image reconstruction Global Positioning System
Content Type	Text
Resource Type	Article

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