Shadow detection for mobile robots: Features, evaluation, and datasets

Newey, Charles C.; Jones, Owain D.; Dee, Hannah M.. 2018 Shadow detection for mobile robots: Features, evaluation, and datasets. Spatial Cognition & Computation, 18 (2). 115-137. https://doi.org/10.1080/13875868.2017.1322088

Before downloading, please read NORA policies.

Preview

Text
© Taylor & Francis 2017 This is an Accepted Manuscript of an article published in Spatial Cognition and Computation, available online: http://wwww.tandfonline.com/10.1080/13875868.2017.1322088
13875868.2017.1322088.pdf - Accepted Version
Download (2MB) | Preview

Official URL: https://doi.org/10.1080/13875868.2017.1322088

Abstract/Summary

Shadows have long been a challenging topic for computer vision. This challenge is made even harder when we assume that the camera is moving, as many existing shadow detection techniques require the creation and maintenance of a background model. This article explores the problem of shadow modelling from a moving viewpoint (assumed to be a robotic platform) through comparing shadow-variant and shadow-invariant image features — primarily color, texture and edge-based features. These features are then embedded in a segmentation pipeline that provides predictions on shadow status, using minimal temporal context. We also release a public dataset of shadow-related image sequences, to help other researchers further develop shadow detection methods and to enable benchmarking of techniques.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1080/13875868.2017.1322088
ISSN:	1387-5868
Date made live:	22 Aug 2017 13:09 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/517654

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1080/13875868.2017.1322088

ISSN:

1387-5868

Date made live:

22 Aug 2017 13:09 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/517654