Background subtraction using Gaussian Mixture Model enhanced by Hole Filling Algorithm (GMMHF)

Adi Nurhadiyatna, Wisnu Jatmiko, Benny Hardjono, Ari Wibisono, Ibnu Sina, Petrus Mursanto

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

24 Citations (Scopus)

Abstract

There is a necessity in traffic control system using camera to have the capability to discriminate between an object and non-object in the image. One of the procedure to discriminate between those two is usually performed by background subtraction. Gaussian Mixture Model (GMM) is popular method that has been employed to tackle the problem of background subtraction. However, the output of GMM is a rather noisy image which comes from false classification. This situation may arise because several conditions in the video input such as, waving trees, rippling water, and illumination changes. In this paper, an enhanced version of GMM technique which is combined with Hole Filling Algorithm (HF) is proposed to alleviate those problems. The experimental result shows that the proposed method improved the accuracy up to 97.9% and Kappa statistic up to 0.74. This result has outperformed many similar methods that is used for evaluation.

Original languageEnglish
Title of host publicationProceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013
Pages4006-4011
Number of pages6
DOIs
Publication statusPublished - 1 Dec 2013
Event2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013 - Manchester, United Kingdom
Duration: 13 Oct 201316 Oct 2013

Publication series

NameProceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013

Conference

Conference2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013
Country/TerritoryUnited Kingdom
CityManchester
Period13/10/1316/10/13

Keywords

  • Background subtraction
  • Gaussian Mixture Model
  • Hole Filling Algorithm

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