Modeling depth for nonparametric foreground segmentation using RGBD devices

Gabriel Moyà-Alcover, Ahmed Elgammal, Antoni Jaume-i-Capó, Javier Varona

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

The problem of detecting changes in a scene and segmenting the foreground from background is still challenging, despite previous work. Moreover, new RGBD capturing devices include depth cues, which could be incorporated to improve foreground segmentation. In this work, we present a new nonparametric approach where a unified model mixes the device multiple information cues. In order to unify all the device channel cues, a new probabilistic depth data model is also proposed where we show how to handle the inaccurate data to improve foreground segmentation. A new RGBD video dataset is presented in order to introduce a new standard for comparison purposes of this kind of algorithms. Results show that the proposed approach can handle several practical situations and obtain good results in all cases.

Original languageEnglish (US)
Pages (from-to)76-85
Number of pages10
JournalPattern Recognition Letters
Volume96
DOIs
StatePublished - Sep 1 2017

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Keywords

  • Absent depth observations
  • Background subtraction
  • Moving object detection
  • Non-parametric estimation
  • RGBD dataset

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