Adaptive model for background extraction using depth map

Boyuan Sun*, Tammam Tillo, Ming Xu

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

1 Citation (Scopus)

Abstract

Depth map has attracted great attention for image and video processing in recent years. Depth map gives one more dimensional information about the images besides color (intensity). Depth is independent of color, which is the advantage for extracting the background covered by objects with irregular repetitive motions e.g. rotation. A new algorithm for background extraction using Gaussian Mixture Models (GMM) combined with depth map is presented. The per-pixel mixture model and single Gaussian model are used to model the recent observation in color and depth space respectively. We also incorporate the color-depth consistency check mechanism into the algorithm to improve the accuracy. Our results show much greater robustness than prior state of the art method to handle challenging scenes.

Original languageEnglish
Title of host publicationAdvances in Multimedia Information Processing – PCM 2015 - 16th Pacific-Rim Conference on Multimedia, Proceedings
EditorsYo-Sung Ho, Yong Man Ro, Junmo Kim, Fei Wu, Jitao Sang
PublisherSpringer Verlag
Pages419-427
Number of pages9
ISBN (Print)9783319240770
DOIs
Publication statusPublished - 2015
Event16th Pacific-Rim Conference on Multimedia, PCM 2015 - Gwangju, Korea, Republic of
Duration: 16 Sept 201518 Sept 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9315
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th Pacific-Rim Conference on Multimedia, PCM 2015
Country/TerritoryKorea, Republic of
CityGwangju
Period16/09/1518/09/15

Keywords

  • Background extraction
  • Depth map
  • Gaussian mixture model

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