Authors:
Luke Thomas
1
;
Matt Roach
1
;
Alma Rahat
1
;
Austin Capsey
2
and
Mike Edwards
1
Affiliations:
1
Swansea University, Swansea, U.K.
;
2
UK Hydrographics Office, Taunton, Somerset, U.K.
Keyword(s):
Place Recognition, Waterborne Imagery, Region Proposal, Image Segmentation, Unsupervised Learning.
Abstract:
To tackle specific challenges of place recognition in the shoreline image domain, we develop a novel Deep Visual Place Recognition pipeline minimizing redundant feature extraction and maximizing salient feature extraction by exploiting the shoreline horizon. Optimizing for model performance and scalability, we present Semantic and Horizon-Based Matching for Visual Place Recognition (SHM-VPR). Our approach is motivated by the unique nature of waterborne imagery, namely the tendency for salient land features to make up a minority of the overall image, with the rest being disposable sea and sky regions. We initially attempt to exploit this via unsupervised region proposal, but we later propose a horizon-based approach that provides improved performance. We provide objective results on both a novel in-house shoreline dataset and the already established Symphony Lake dataset, with SHM-VPR providing state-of-the-art results on the former.