Information system department, University of Haifa, Israel, firstname.lastname@example.org With the growth of the commercial interest in indoor Location-based Services (ILBS), a lot of effort was put into the development of indoor positioning systems. One way to track users in indoor environment is using image-based localization. The user captures an image in front of a desirable place and the system locates him. Such systems use image matching algorithms, and usually they try to match the current image that was captured by the user with all the images that exist in the dataset. The big challenge is the dataset preparation; it has to be representative and minimal as much as it can be to reduce the number of comparisons. Previous works used special equipment to map or scan the environments, and others used human operators who have expertise in image matching algorithms. In this work, we present ARIDF, an automated method that finds a minimal and representative dataset for image-based localization. The human operators should not have any previous knowledge and experience about image matching algorithms.
|Title of host publication||UMAP2022 - Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||8|
|State||Published - 4 Jul 2022|
|Event||30th ACM Conference on User Modeling, Adaptation and Personalization, UMAP2022 - Virtual, Online, Spain|
Duration: 4 Jul 2022 → 7 Jul 2022
|Name||UMAP2022 - Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization|
|Conference||30th ACM Conference on User Modeling, Adaptation and Personalization, UMAP2022|
|Period||4/07/22 → 7/07/22|
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