Spatially adaptive illumination modeling for background subtraction

Jithendra K. Paruchuri, Edwin P. Sathiyamoorthy, Sen Ching S. Cheung, Chung Hao Chen

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

13 Scopus citations

Abstract

Background subtraction is important for many vision applications. Existing techniques can adapt to gradual changes in illumination but fail to cope with sudden changes often seen in indoor environment. In this paper, we propose a novel background subtraction technique that models the change of illumination as a regression function of spatial image coordinates. Such spatial dependency is significant when light sources are close to or within the scene. The regression function is learned from highly probable background regions and applied to the rest of the background models to compensate for the illumination change. While a single regression function is adequate for a smooth Lambertian surface, multiple regression functions are needed to handle depth discontinuities, shadows, and non-Lambertian surfaces. The change of illumination is first segmented and different regression functions are applied to different segments. Experimental results comparing our techniques with other schemes show better foreground segmentation during illumination change.

Original languageEnglish
Title of host publication2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011
Pages1745-1752
Number of pages8
DOIs
StatePublished - 2011
Event2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011 - Barcelona, Spain
Duration: Nov 6 2011Nov 13 2011

Publication series

NameProceedings of the IEEE International Conference on Computer Vision

Conference

Conference2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011
Country/TerritorySpain
CityBarcelona
Period11/6/1111/13/11

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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