Real-time Object Recognition with Pose Initialization for Large-scale Standalone Mobile Augmented Reality

被引:0
|
作者
Lee, Suwon [1 ,2 ]
机构
[1] Gyeongsang Natl Univ, Dept Comp Sci, 501 Jinjudae Ro, Jinju Si, Gyeongsangnam D, South Korea
[2] Gyeongsang Natl Univ, Res Inst Nat Sci, 501 Jinjudae Ro, Jinju Si, Gyeongsangnam D, South Korea
基金
新加坡国家研究基金会;
关键词
Real-time object recognition; mobile augmented reality; large-scale object recognition; standalone augmented reality; real-time feature matching; TRACKING;
D O I
10.3837/tiis.2020.10.010
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Mobile devices such as smartphones are very attractive targets for augmented reality (AR) services, but their limited resources make it difficult to increase the number of objects to be recognized. When the recognition process is scaled to a large number of objects, it typically requires significant computation time and memory. Therefore, most large-scale mobile AR systems rely on a server to outsource recognition process to a high-performance PC, but this limits the scenarios available in the AR services. As a part of realizing large-scale standalone mobile AR, this paper presents a solution to the problem of accuracy, memory, and speed for large-scale object recognition. To this end, we design our own basic feature and realize spatial locality, selective feature extraction, rough pose estimation, and selective feature matching. Experiments are performed to verify the appropriateness of the proposed method for realizing large-scale standalone mobile AR in terms of efficiency and accuracy.
引用
收藏
页码:4098 / 4116
页数:19
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