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

Newey, Charles C.; Jones, Owain D.; Dee, Hannah M.. 2017 Shadow detection for mobile robots: Features, evaluation, and datasets. Spatial Cognition & Computation. 10.1080/13875868.2017.1322088 (In Press)

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© 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
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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): 10.1080/13875868.2017.1322088
ISSN: 1387-5868
Date made live: 22 Aug 2017 13:09 +0 (UTC)
URI: http://nora.nerc.ac.uk/id/eprint/517654

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