Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/12340
Title: Efficient search in a panoramic image database for long-term visual localization
Authors: Orhan, Semih
Baştanlar, Yalın
Keywords: Image retrieval
Database images
Issue Date: 2021
Publisher: IEEE
Abstract: In this work, we focus on a localization technique that is based on image retrieval. In this technique, database images are kept with GPS coordinates and the geographic location of the retrieved database image serves as an approximate position of the query image. In our scenario, database consists of panoramic images (e.g. Google Street View) and query images are collected with a standard field-of-view camera in a different time. While searching the match of a perspective query image in a panoramic image database, unlike previous studies, we do not generate a number of perspective images from the panoramic image. Instead, taking advantage of CNNs, we slide a search window in the last convolutional layer belonging to the panoramic image and compute the similarity with the descriptor extracted from the query image. In this way, more locations are visited in less amount of time. We conducted experiments with state-of-the-art descriptors and results reveal that the proposed sliding window approach reaches higher accuracy than generating 4 or 8 perspective images.
Description: 18th IEEE/CVF International Conference on Computer Vision (ICCV) -- OCT 11-17, 2021
URI: https://doi.org/10.1109/ICCVW54120.2021.00198
https://hdl.handle.net/11147/12340
ISBN: 978-1-6654-0191-3
ISSN: 2473-9936
Appears in Collections:Computer Engineering / Bilgisayar Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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