TextPlace: Visual Place Recognition and Topological Localization Through Reading Scene Texts

Ziyang Hong, Yvan Petillot, David Lane, Yishu Miao, Sen Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)
71 Downloads (Pure)

Abstract

Visual place recognition is a fundamental problem for many vision based applications. Sparse feature and deep learning based methods have been successful and dominant over the decade. However, most of them do not explicitly leverage high-level semantic information to deal with challenging scenarios where they may fail. This paper proposes a novel visual place recognition algorithm, termed TextPlace, based on scene texts in the wild. Since scene texts are high-level information invariant to illumination changes and very distinct for different places when considering spatial correlation, it is beneficial for visual place recognition tasks under extreme appearance changes and perceptual aliasing. It also takes spatial-temporal dependence between scene texts into account for topological localization. Extensive experiments show that TextPlace achieves state-of-the-art performance, verifying the effectiveness of using high-level scene texts for robust visual place recognition in urban areas.
Original languageEnglish
Title of host publication2019 IEEE/CVF International Conference on Computer Vision (ICCV)
PublisherIEEE
Pages2861-2870
Number of pages10
ISBN (Electronic)9781728148038
DOIs
Publication statusPublished - 27 Feb 2020

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