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http://dx.doi.org/10.3837/tiis.2020.10.010

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

Lee, Suwon (Department of Computer Science and The Research Institute of Natural Science, Gyeongsang National University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.14, no.10, 2020 , pp. 4098-4116 More about this Journal
Abstract
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.
Keywords
Real-time object recognition; mobile augmented reality; large-scale object recognition; standalone augmented reality; real-time feature matching;
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