Adaptive threshold for background subtraction in moving object detection using Fuzzy C-Means clustering

Moch Arief Soeleman*, Mochamad Hariadi, Mauridhi Hery Purnomo

*Corresponding author for this work

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

20 Citations (Scopus)

Abstract

Background subtraction is the important part of moving object detection. The problem of background subtraction is threshold selection strategy. This paper proposed a Fuzzy C-Means (FCM) algorithm to produce an adaptive threshold for background subtraction in moving object detection. To evaluate the performance, FCM were compared against standard Otsu algorithm as threshold selection strategy. Mean Square Error (MSE) and Peak Signal Noise Ratio (PSNR) was used to measure the performance. Based on the experiment, the MSE of FCM is lower than MSE of Otsu and PSNR of FCM is higher than PSNR of Otsu. The result proved that FCM is promising to classify the pixels as foreground or background in moving object detection.

Original languageEnglish
Title of host publicationIEEE TENCON 2012
Subtitle of host publicationSustainable Development Through Humanitarian Technology
DOIs
Publication statusPublished - 2012
Event2012 IEEE Region 10 Conference: Sustainable Development Through Humanitarian Technology, TENCON 2012 - Cebu, Philippines
Duration: 19 Nov 201222 Nov 2012

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON

Conference

Conference2012 IEEE Region 10 Conference: Sustainable Development Through Humanitarian Technology, TENCON 2012
Country/TerritoryPhilippines
CityCebu
Period19/11/1222/11/12

Keywords

  • fuzzy c-means
  • moving object segmentation
  • otsu algorithm

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