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The Oxford Road Boundaries Dataset

2021-06-16 17:23:34
Tarlan Suleymanov, Matthew Gadd, Daniele De Martini, Paul Newman
arXiv_CV
       
Abstract

In this paper we present the Oxford Road Boundaries Dataset, designed for training and testing machine-learning-based road-boundary detection and inference approaches. We have hand-annotated two of the 10 km-long forays from the Oxford Robotcar Dataset and generated from other forays several thousand further examples with semi-annotated road-boundary masks. To boost the number of training samples in this way, we used a vision-based localiser to project labels from the annotated datasets to other traversals at different times and weather conditions. As a result, we release 62605 labelled samples, of which 47639 samples are curated. Each of these samples contains both raw and classified masks for left and right lenses. Our data contains images from a diverse set of scenarios such as straight roads, parked cars, junctions, etc. Files for download and tools for manipulating the labelled data are available at: this http URL

Abstract (translated)

URL

https://arxiv.org/abs/2106.08983

PDF

https://arxiv.org/pdf/2106.08983.pdf

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