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oapen-20.500.12657-890342024-04-03T02:22:40Z Chapter Optimal Number of Cue Objects for Photo-Based Indoor Localization Chung, Youngsun Gil, Daeyoung Lee, Ghang indoor location determination BIM reasoning thema EDItEUR::U Computing and Information Technology::UT Computer networking and communications::UTV Virtualization 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 2024-04-02T15:44:17Z 2024-04-02T15:44:17Z 2023 chapter ONIX_20240402_9791221502893_3 2704-5846 9791221502893 https://library.oapen.org/handle/20.500.12657/89034 eng Proceedings e report application/pdf n/a 9791221502893_98.pdf https://books.fupress.com/doi/capitoli/979-12-215-0289-3_98 Firenze University Press 10.36253/979-12-215-0289-3.98 10.36253/979-12-215-0289-3.98 bf65d21a-78e5-4ba2-983a-dbfa90962870 9791221502893 137 11 Florence open access
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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
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