Open AccessFast and Accurate Visual Place Recognition Using Street‐View Images
Author(s) -
Lee Keundong,
Lee Seungjae,
Jung Won Jo,
Kim Kee Tae
Publication year - 2017
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.17.0116.0034
Subject(s) - computer science , artificial intelligence , computer vision , identification (biology) , precision and recall , matching (statistics) , image retrieval , ambiguity , image (mathematics) , visualization , pattern recognition (psychology) , mathematics , statistics , botany , biology , programming language
A fast and accurate building‐level visual place recognition method built on an image‐retrieval scheme using street‐view images is proposed. Reference images generated from street‐view images usually depict multiple buildings and confusing regions, such as roads, sky, and vehicles, which degrades retrieval accuracy and causes matching ambiguity. The proposed practical database refinement method uses informative reference image and keypoint selection. For database refinement, the method uses a spatial layout of the buildings in the reference image, specifically a building‐identification mask image, which is obtained from a prebuilt three‐dimensional model of the site. A global‐positioning‐system‐aware retrieval structure is incorporated in it. To evaluate the method, we constructed a dataset over an area of 0.26 km 2 . It was comprised of 38,700 reference images and corresponding building‐identification mask images. The proposed method removed 25% of the database images using informative reference image selection. It achieved 85.6% recall of the top five candidates in 1.25 s of full processing. The method thus achieved high accuracy at a low computational complexity.