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

Charles Newey, Owain Jones, Hannah Dee

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
192 Downloads (Pure)

Abstract

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.
Original languageEnglish
Pages (from-to)1-23
Number of pages23
JournalSpatial Cognition and Computation
DOIs
Publication statusPublished - 28 Apr 2017

Keywords

  • vision and natural language
  • visual perception
  • robotics

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