Abstract
The benefits of technologies related to the Internet of Things (IoT), virtual and augmented reality (VR/AR), digital twins, and so on, can be fully realized when associated devices are positioned intuitively. However, AR systems hosted within smartphones pose challenges where auxiliary hardware and computational configurations associated with precise positioning are concerned. To this effect, we propose a deep learning-based indoor measurement system that can determine positions using images collected via beacons designed as IoT terminals. The proposed system is broadly divided into a detection unit, an extraction unit, a positioning unit, and a management server. The beacons were detected using deep learning algorithms, from which the postures were extracted using a homography matrix, and position of the imaging device was determined in reference to the beacon’s position. With the unique design of our system, in that it simultaneously performs posture and positioning estimations, high immersive AR can be achieved. Moreover, scalability of the positioning space is also guaranteed as multiple beacons can be monitored at once. For the experiment, we simulated a virtual indoor space comprising pyramid beacons and the results were promising.
Similar content being viewed by others
References
Beauregard S, Haas H (2006) Pedestrian dead reckoning: a basis for personal positioning. In: proceedings of the 3rd workshop on positioning, navigation and communication. Pp 27–35
Chum O, Pajdla T, Sturm P (2005) The geometric error for homographies. Comput Vis Image Underst 97:86–102
Dai J, Li Y, He K, Sun J (2016) R-FCN: object detection via region-based fully convolutional networks. In: Lee DD, Sugiyama M, Luxburg UV, et al (eds) Advances in neural information processing systems 29. Curran Associates, Inc., pp. 379–387
Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 580–587
Ha H, Rameau F, Kweon IS (2016) 6-DOF direct Homography tracking with extended Kalman filter. In: Image and Video Technology. Springer International Publishing, pp. 447–460
Jo D, Kim GJ (2019) IoT+ AR: pervasive and augmented environments for “Digi-log” shopping experience. Human-centric Computing and Information Sciences 9:1–17
Khongkraphan K (2019) An efficient fingerprint matching by multiple reference points. Journal of Information Processing Systems 15:
Komine T, Nakagawa M (2004) Fundamental analysis for visible-light communication system using LED lights. IEEE Trans Consum Electron 50:100–107
Lee SW, Kim SW (2015) Indoor positioning technology trends and outlook. The Journal of the korean institute of communication sciences 32:81–88
Lee S, Kim J, Moon N (2019) Random forest and WiFi fingerprint-based indoor location recognition system using smart watch. Human-centric Computing and Information Sciences 9:6
Li Y, Ghassemlooy Z, Tang X, Lin B, Zhang Y (2018) A VLC smartphone camera based indoor positioning system. IEEE Photon Technol Lett 30:1171–1174
Lu C, Hager GD, Mjolsness E (2000) Fast and globally convergent pose estimation from video images. IEEE Trans Pattern Anal Mach Intell 22:610–622
Luo P, Zhang M, Zhang X, et al (2013) An indoor visible light communication positioning system using dual-tone multi-frequency technique. In: 2013 2nd international workshop on optical wireless communications (IWOW). Pp 25–29
Mautz R, Tilch S (2011) Survey of optical indoor positioning systems. In: 2011 international conference on indoor positioning and indoor navigation. Pp 1–7
Redmon J, Farhadi A (2018) YOLOv3: an incremental improvement. arXiv [cs.CV]
Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39:1137–1149
Slabaugh GG (1999) Computing Euler angles from a rotation matrix. Retrieved on August
Szegedy C, Liu W, Jia Y, et al (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1–9
Wang X-X, Zhao X-M, Shen Y (2019) A Video Traffic Flow Detection System Based on Machine Vision Journal of Information Processing Systems 15:
Werner M, Kessel M, Marouane C (2011) Indoor positioning using smartphone camera. In: 2011 international conference on indoor positioning and indoor navigation. Pp 1–6
Zhang X, Rad AB, Wong Y-K (2012) Sensor fusion of monocular cameras and laser rangefinders for line-based simultaneous localization and mapping (SLAM) tasks in autonomous mobile robots. Sensors 12:429–452
Zhang S, Wen L, Bian X, et al (2018) Single-shot refinement neural network for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4203–4212
Zhao Z-Q, Zheng P, Xu S-T, Wu X (2019) Object detection with deep learning: a review. IEEE Trans Neural Netw Learn Syst 30:3212–3232
Acknowledgments
This work is supported by the National Research Foundation of Korea (NRF) and the grant was funded by the Korean Government (MSIT, No. NRF-2017R1A2B4008886).
We would like to thank Editage (www.editage.co.kr) for English language editing.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
An, H., Moon, N. Image-based positioning system using LED Beacon based on IoT central management. Multimed Tools Appl 81, 26655–26667 (2022). https://doi.org/10.1007/s11042-020-10166-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-020-10166-3