Vol. 14, No. 10, October 31, 2020
10.3837/tiis.2020.10.010,
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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.
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Cite this article
[IEEE Style]
S. Lee, "Real-time Object Recognition with Pose Initialization for Large-scale Standalone Mobile Augmented Reality," KSII Transactions on Internet and Information Systems, vol. 14, no. 10, pp. 4098-4116, 2020. DOI: 10.3837/tiis.2020.10.010.
[ACM Style]
Suwon Lee. 2020. Real-time Object Recognition with Pose Initialization for Large-scale Standalone Mobile Augmented Reality. KSII Transactions on Internet and Information Systems, 14, 10, (2020), 4098-4116. DOI: 10.3837/tiis.2020.10.010.
[BibTeX Style]
@article{tiis:23922, title="Real-time Object Recognition with Pose Initialization for Large-scale Standalone Mobile Augmented Reality", author="Suwon Lee and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2020.10.010}, volume={14}, number={10}, year="2020", month={October}, pages={4098-4116}}