Contained in:
Book Chapter

Optimal Number of Cue Objects for Photo-Based Indoor Localization

  • Youngsun Chung
  • Daeyoung Gil
  • Ghang Lee

Building information modeling (BIM) is widely used to generate indoor images for indoor localization. However, changes in camera angles and indoor conditions mean that photos are much more changeable than BIM images. This makes any attempt at localization based on the similarity between real photos and BIM images challenging. To overcome this limitation, we propose a reasoning-based approach for determining the location of a photo by detecting the cue objects in the photo and the relationships between them. The aim of this preliminary study was to determine the optimal number of cue objects required for an indoor image. If there are too few cue objects in an indoor image, it results in an excessive number of location candidates. Conversely, if there are too many cue objects, the accuracy of object detection in an image decreases. Theoretically, a larger number of cue objects would improve the reasoning process; however, too many cue objects could lead to declining object detection performance. The experimental results demonstrated that of two to five cue objects, three cue objects is most likely to yield optimal performance

  • Keywords:
  • indoor location determination,
  • BIM,
  • reasoning,
+ Show More

Youngsun Chung

Yonsei University, Korea (the Republic of)

Daeyoung Gil

Yonsei University, Korea (the Republic of) - ORCID: 0000-0001-9845-6687

Ghang Lee

Yonsei University, Korea (the Republic of) - ORCID: 0000-0002-3522-2733

  1. Acharya, D., Khoshelham, K., & Winter, S. (2019). BIM-PoseNet: Indoor camera localisation using a 3D indoor model and deep learning from synthetic images. ISPRS Journal of Photogrammetry and Remote Sensing, 150, 245–258. DOI: 10.1016/j.isprsjprs.2019.02.020
  2. Acharya, D., Roy, S., Khoshelham, K., & Winter, S. (2020). A recurrent deep network for estimating the pose of real indoor images from synthetic image sequences. Sensors, 20(19), 1–20. DOI: 10.3390/s20195492
  3. Alam, M., Hossain, A. K. M., & Mohamed, F. (2022). Performance evaluation of recurrent neural networks applied to indoor camera localization. International Journal of Emerging Technology and Advanced Engineering, 12(8), 116–124. DOI: 10.46338/ijetae0822_15
  4. Bay, H., Tuytelaars, T., & Van Gool, L. (2006). SURF: Speeded up robust features. In A. Leonardis, H. Bischof, & A. Pinz (Eds.), Computer Vision – ECCV 2006 (pp. 404–417). Springer. DOI: 10.1007/11744023_32
  5. Guan, K., Ma, L., Tan, X., & Guo, S. (2016). Vision-based indoor localization approach based on SURF and landmark. 2016 International Wireless Communications and Mobile Computing Conference (IWCMC), (pp. 655–659). DOI: 10.1109/IWCMC.2016.7577134
  6. Ha, I., Kim, H., Park, S., & Kim, H. (2018). Image retrieval using BIM and features from a pretrained VGG network for indoor localization. Building and Environment, 140, 23–31. DOI: 10.1016/j.buildenv.2018.05.026
  7. Kang, H., Park, Y., & Kim, Y. (2019). Improvement model of defect information management system for apartment buildings. Korean Journal of Construction Engineering and Management, 20(4), 13–21. DOI: 10.6106/KJCEM.2019.20.4.013
  8. Kim, D., & Kim, J. (2023). CT-Loc: Cross-domain visual localization with a channel-wise transformer. Neural Networks, 158, 369–383. DOI: 10.1016/j.neunet.2022.11.014
  9. Kim, J. (2022). Identifying indoor locations of close-up photos using deep learning and building information modeling objects. Yonsei University. http://www.riss.kr/link?id=T16372630
  10. Kim, K.-T., Lim, M.-G., & Kim, G.-T. (2014). History management technology of building construction and maintenance using vector photo information and BIM. Journal of the Korea Institute of Building Construction, 14(6), 605–613. DOI: 10.5345/JKIBC.2014.14.6.605
  11. Li, Y., Kambhamettu, R. H., Hu, Y., & Zhang, R. (2022). ImPos: An image-based indoor positioning system. 2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC), (pp. 144–150). DOI: 10.1109/CCNC49033.2022.9700699
  12. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. Computer Vision and Pattern Recognition Conference (CVPR), (pp. 779-788). https://openaccess.thecvf.com/content_cvpr_2016/html/Redmon_You_Only_Look_CVPR_2016_paper.html
  13. Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. arXiv. DOI: 10.48550/arXiv.1409.1556
PDF
  • Publication Year: 2023
  • Pages: 977-987

XML
  • Publication Year: 2023

Chapter Information

Chapter Title

Optimal Number of Cue Objects for Photo-Based Indoor Localization

Authors

Youngsun Chung, Daeyoung Gil, Ghang Lee

DOI

10.36253/979-12-215-0289-3.98

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Bibliographic Information

Book Title

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

Book Subtitle

Managing the Digital Transformation of Construction Industry

Editors

Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press

DOI

10.36253/979-12-215-0289-3

eISBN (pdf)

979-12-215-0289-3

eISBN (xml)

979-12-215-0257-2

Series Title

Proceedings e report

Series ISSN

2704-601X

Series E-ISSN

2704-5846

42

Fulltext
downloads

31

Views

Export Citation

1,310

Open Access Books

in the Catalogue

1,977

Book Chapters

3,390,281

Fulltext
downloads

4,172

Authors

from 873 Research Institutions

of 64 Nations

63

scientific boards

from 340 Research Institutions

of 43 Nations

1,159

Referees

from 345 Research Institutions

of 37 Nations